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At Pfizer, technology accelerates every facet of our mission. Within the AI Center of Excellence, the AI Quality Engineering team embeds trust, quality, and responsibility into every AI solution we deliver.As a Staff QA Engineer, you will lead the design and operation of enterprise‑grade evaluation frameworks, guardrails, and continuous quality pipelines that keep our models performant and safe. Your work enables teams across the company to ship AI solutions with confidence—meeting rigorous standards for accuracy, fairness, security, and compliance while driving measurable impact for patients and colleagues alike.Why this Role MattersThis role is pivotal in shaping the organization’s AI strategy and technical excellence. Your leadership will accelerate the adoption of cutting-edge AI technologies, foster a culture of innovation, and ensure that AI solutions are safe, reliable, and impactful at scale.ROLE RESPONSIBILITIES<ul><li>Define &amp; lead enterprise AI evaluation frameworks for model performance, robustness, fairness, bias, and safety across LLMs, traditional ML, and agentic AI systems.</li><li>Architect and operate pre‑release &amp; continuous evaluation (batch + online) integrated with CI/CD, including dataset versioning, synthetic test generation, and production quality gates.</li><li>Design, implement, and maintain guardrail services for hallucinations, prompt injection, PII leakage, unsafe content, and policy violations; instrument telemetry for detection, alerting, and drift tracking.</li><li>Run structured red‑teaming programs (jailbreaks, data exfiltration, adversarial prompts) and drive measurable mitigation with engineering teams; publish risk findings and remediation plans.</li><li>Encode quality &amp; compliance policies as automated checks, partnering with Enterprise Architecture and Compliance to meet internal standards and emerging regulations (e.g., EU AI Act).</li><li>Establish cross‑use‑case quality SLOs &amp; dashboards, reporting enterprise‑wide quality and safety metrics; lead post‑incident reviews and prevention programs.</li><li>Mentor QA/AI engineers on evaluation science, test design, and responsible AI practices; uplift engineering excellence and documentation quality across teams.</li><li>Collaborate with AI Engineering, Integrations &amp; Enablement teams to ensure evaluation hooks, observability, and policy gates are first‑class in our SDKs, APIs, and shared components.</li><li>Collaborate with AI Innovation team on continuously assess emerging evaluation methods &amp; tools, run rapid prototypes and PoCs, and advise on strategic adoption that improves quality, safety, and developer experience.</li></ul>BASIC QUALIFICATIONS<ul><li>Advanced degree in Computer Science, Engineering, Data Science, or related field.</li><li>7+ years in Software QA/AI QA/Quality Engineering, including ownership of test strategy for distributed or data‑intensive systems</li><li>Demonstrated experience building evaluation pipelines for ML/LLM systems: design of datasets/benchmarks, metrics for accuracy/robustness, and failure‑mode analysis (e.g., hallucination, bias, toxicity).</li><li>Strong fluency in software engineering best practices (automated testing, version control, CI/CD) and observability (logging, metrics, tracing) for model‑centric applications.</li><li>Hands‑on with MLOps concepts (model serving, deployment, feature/data versioning) and policy gating in release pipelines.</li><li>Ability to produce clear technical documentation (evaluation protocols, runbooks, API references) and developer‑friendly guides that scale adoption.</li></ul>PREFERRED QUALIFICATIONS<ul><li>Experience in pharma/healthcare or other regulated environments (validation, auditability, GxP-adjacent controls).</li><li>Deep knowledge of LLM safety &amp; evaluation techniques: prompt‑injection defense, content moderation, RAG evaluation, context integrity, and bias/fairness testing.</li><li>Practical experience with evaluation libraries &amp; frameworks (e.g., Ragas, TruLens, DeepEval, OpenAI Evals) and guardrail tooling integrated with platform SDKs.</li><li>Familiarity with agentic AI behaviors and evaluation, including planner/tool‑use reliability, memory safety, and multi‑agent orchestration checks.</li><li>Experience leading red‑team programs and incident response; evidence of measurable risk reduction and improvement in safety KPIs. (Competency expectations at Staff level)</li><li>Contribution to open‑source or industry forums on Responsible AI, safety evaluation, or testing.</li></ul>&nbsp;

Staff QA Engineer 4949292

The Techno Functional Digital Master Data Solutions role supports functional and technical aspects of Global Digital ERP Master Data initiatives at Pfizer. Responsibilities include managing change requests and projects, delivering solutions through documentation, design, development, and testing in collaboration with diverse stakeholders across Pfizer's business and Digital teams. &nbsp;The successful candidate will possess robust functional and technical expertise, a thorough understanding of Agile methodologies, a commitment to continuous learning in digital technologies and solutions, and an interest in Material Master data governance across functions within a dynamic, collaborative, and fast-paced environment.<ul><li>Oversee Global Digital ERP Master Data Solutions to support Pfizer’s ongoing and new Master Data initiatives.</li><li>Ensure the delivery, availability, user experience, and continuous improvement of solutions.</li><li>Manage financial and project aspects of Digital initiatives, including the maintenance and updating of all Pfizer systems.</li><li>Implement, maintain, and assess Digital support strategies for Master Data in partnership with the ERP Support team.</li><li>Lead the design, development, and implementation of ERP Digital SAP Master Data Solutions, encompassing Bill of Material Engineering Change Management, Packaging Specifications, Lot Variable Control Document, Workflow functionality, integration with Manufacturing systems, Material Master, SAP MDG functionality, and SAP S4 Master Data functionality.</li><li>Provide regular updates to ensure alignment on strategies, priorities, status, risks, and mitigation plans.</li><li>Collaborate with business engagement and technical development teams to architect effective, efficient, and fit-for-purpose solutions that enable business capability and regulatory compliance.</li><li>Direct multi-disciplinary matrix project teams in the implementation of projects, maintaining resource plans and system updates.</li><li>Review business requirements with stakeholders to ensure accuracy, detail, and clarity, fostering a comprehensive understanding of process, people, and technology impacts.</li><li>Deliver solutions in accordance with Pfizer Digital standards, including SDLC, Enterprise Platforms &amp; Security, Digital Compliance, and Regulatory Risk Management.</li><li>Guide and approve training package development for SAP Master Data Solutions.</li><li>Drive exceptional execution across a diverse portfolio of solutions spanning multiple applications, technologies—including SAP and non-SAP platforms—and emerging digital tools.</li><li>Collect requirements, perform GAP analysis, specify system integration points, and oversee overall system design to establish fully functional development, testing, and production environments.</li><li>Coordinate activities among cross-functional teams distributed across various geographical locations.</li><li>Translate high-level business needs into detailed functional and technical specifications.</li><li>Manage the design, configuration, development, testing, and implementation phases of projects.</li><li>Provide technical and functional guidance to internal Pfizer clients, including teams focused on data, infrastructure, integration, and security.</li></ul>Here Is What You Need (Minimum Requirements)<ul><li>Bachelor’s degree in computer science, engineering, or supply chain.</li><li>Minimum 5 years’ relevant technology experience, including SAP Master Data (Material Master, MDG, BOM, ECM, Workflows, EHS module).</li><li>Experience with O365 and collaboration tools.</li><li>Proven track record leading global digital projects in pharma.</li><li>Strong project and financial management skills.</li><li>Demonstrated consultative, strategic, analytical, and problem-solving abilities.</li><li>Effective oral, written communication and presentation skills.</li><li>Leadership skills: influence, collaboration, coaching, and overseeing teams to achieve business outcomes.</li></ul>Bonus Points If You Have (Preferred Requirements)<ul><li>Experience with S4 HANA and SAP MDG for master data configuration.</li><li>Knowledge of SAP programming, Fiori Apps, OData, BADI/BAPI debugging.</li><li>Programming language proficiency.</li><li>Configuration and functional customization expertise.</li><li>Experience with web services (REST, SOAP).</li><li>Supply chain or project management certifications (CPIM, CSCP, PMP); project management experience a plus.</li><li>Agile Software Delivery and/or Scrum Master certification.</li><li>Strong knowledge of GMP IT system design and maintenance lifecycle </li></ul>

Techno Functional Manager - Digital Master Data Solutions 4948248

As Manager, Software Engineer (Individual Contributor), you will contribute to the design and development of modular AI capabilities within the Enterprise AI Capability Design Team. Your focus will be on implementing SDK components, interfaces, and supporting modules that enable internal teams and the broader community to integrate AI capabilities seamlessly into applications. You will work under the guidance of senior engineers and architects, gaining exposure to API design, modular architecture, and cloud-native development while progressively taking ownership of components.You will collaborate closely with:<ul><li>Embedded AI Architecture Team and Solution Design Team to ensure SDK interfaces align with real-world solution patterns.</li><li>AI Engineering Team for PI increments and integration of external technologies.</li><li>Digital Creation Centers and Forward Impact Teams (FITs) to capture feedback and improve usability.</li></ul>This role is designed for engineers with strong software development experience and a growth mindset, eager to deepen expertise in AI engineering, system design, and SDK architecture.Why this Role MattersManager-level Software Engineers are the builders and maintainers of our AI capability SDK. By delivering well-structured, tested, and documented components, you enable reuse, scalability, and developer experience excellence—critical for accelerating AI adoption across the enterprise.ROLE RESPONSIBILITIES1) SDK Component Development<ul><li>Implement modular SDK components for AI capabilities, including:<ul><li>RAG pipeline elements (parsers, chunkers, enrichers, retrievers, connectors)</li><li>Agent and tool cookiecutters with MCP adaptors</li><li>Interfaces for community-driven extensions</li></ul></li><li>Ensure code adheres to software quality standards for reusability, maintainability, security, readability, and versioning.</li></ul>2) Testing &amp; Quality Assurance<ul><li>Write unit and integration tests for SDK components; collaborate with QA engineers to ensure coverage at various levels (statement / branch / function etc…).</li><li>Integrate logging, metrics, and tracing into SDK components to support observability and troubleshooting.</li><li>Participate in code reviews and maintain compliance with coding standards and security guidelines.</li><li>Contribute to benchmarking and performance validation for new modules.</li></ul>3) Documentation &amp; Developer Experience<ul><li>Produce clear API documentation, usage guides, and code samples for assigned components.</li><li>Support Developer Experience engineers in creating quickstarts and integration guides.</li><li>Ensure documentation aligns with Confluence structure and department standards.</li></ul>4) Collaboration &amp; Continuous Improvement<ul><li>Work closely with Senior Manager, Software Engineer and AI Capability Architecture Lead for design guidance and technical reviews.</li><li>Partner with Embedded AI Architects and Solution Designers to validate SDK interfaces against solution patterns.</li><li>Engage with AI Engineering Team to integrate external technologies and align on PI increments.</li></ul>5) Growth &amp; Skill Development<ul><li>Build proficiency in cloud-native architecture, AI integration patterns, and modular SDK design.</li><li>Participate in knowledge-sharing sessions, retrospectives, and enablement forums.</li><li>Progressively take ownership of more complex components and contribute to architectural discussions.</li></ul>MEASURES OF SUCCESS<ul><li>Component quality: Code meets standards for modularity, maintainability, and performance.</li><li>Testing coverage: High unit/integration test coverage; minimal defects post-release.</li><li>Documentation completeness: Clear API docs, usage guides, and samples delivered with each component.</li><li>Collaboration effectiveness: Positive feedback from senior engineers, architects, and consuming teams.</li><li>Skill progression: Demonstrated growth toward senior-level responsibilities and architectural contributions.</li></ul>QUALIFICATIONSBasic Qualifications<ul><li>5+ years in software engineering roles with experience in API development and modular design.</li><li>Proficiency in Python (required) and familiarity with Java or TypeScript.</li><li>Experience with cloud platforms (Azure, AWS, or GCP)and containerized environments.</li><li>Strong understanding of CI/CD pipelines, automated testing, and code quality practices.</li><li>Ability to produce clear documentation and developer-friendly code samples.</li><li>Proficiency in Git-based workflows (feature branches, PR reviews, semantic versioning)</li></ul>Preferred Qualifications<ul><li>Exposure to AI/ML frameworks (PyTorch, TensorFlow) and GenAI/LLM ecosystems (RAG, vector databases, agent frameworks).</li><li>Contribution to open-source projects and/or community-driven extensions.</li><li>Understanding of observability and security-by-design principles.</li><li>Experience with performance tuning and benchmarking for SDK components.</li></ul>

Manager, Software Engineer 4949164 

As Manager, Technical Writer (Individual Contributor), you will serve as the senior technical writing expert within the AI Architecture &amp; Solution Design department. Your mission is to define and uphold documentation standards and structure for SDKs, modules, and solution patterns, ensuring clarity, consistency, and discoverability across all artefacts. You will work closely with AI Architects, Solution Designers, and Operations Excellence to make documentation a first-class enabler of adoption and reuse.You will lead by expertise, not direct management—acting as the bar-raiser for technical writing quality and mentoring junior writers informally. Beyond documentation, you will contribute to knowledge surfacing and enablement, including indexing solution patterns and supporting tools (e.g., intelligent agents) that help teams find the right SDK or design quickly. You will also play a supporting role in operations feedback, ensuring documentation standards enable operationalizing the SDK and AI Solution patterns across Creation Centers and digital teams.Why this Role MattersDocumentation is the gateway to adoption and reuse. Without clear, indexed, and actionable artefacts, even the best AI capabilities and solution patterns fail to scale. This role ensures that every SDK, module, and architecture pattern is discoverable, understandable, and implementable, reducing bespoke engineering and accelerating time to value.ROLE RESPONSIBILITIES1) Documentation Standards &amp; Structure<ul><li>Define and maintain documentation standards for SDKs, modules, and solution patterns, including required sections (overview, usage, integration steps, evaluation notes, security/privacy considerations).</li><li>Establish and enforce Confluence structure for indexing and surfacing artefacts; ensure logical hierarchy, tagging, and cross-linking for easy navigation.</li><li>Create templates and style guides for architecture artefacts, release notes, and design exemplars; ensure consistency across teams.</li></ul>2) SDK &amp; Module Documentation<ul><li>Partner with Enterprise AI Capability Design Team and community contributors to produce clear, developer-friendly documentation for SDKs and modules.</li><li>Ensure documentation includes code samples, quickstarts, integration guides, and troubleshooting tips; maintain parity between SDK and service documentation.</li><li>Validate documentation completeness during release readiness checks; track gaps and drive remediation.</li></ul>3) Solution Pattern Indexing &amp; Enablement<ul><li>Curate and document solution patterns with supporting material on “when, why, and how” to use them; collaborate with Solution Designers and AI Architects for accuracy.</li><li>Build and maintain a searchable index of patterns and exemplars; ensure discoverability across Confluence and internal portals.</li><li>Contribute to enablement tools (e.g., intelligent agents) that recommend patterns and modules based on user needs, linking back to documentation and design artefacts.</li></ul>4) Operational Feedback &amp; Continuous Improvement<ul><li>Support operations management activities: observe how documentation standards are applied by Creation Centers and digital teams; capture feedback and propose improvements.</li><li>Participate in retrospectives and process reviews; adjust documentation structure and templates to maximize adoption and usability.</li><li>Ensure documentation supports auditability for compliance, security, and governance requirements.</li></ul>5) Informal Mentorship &amp; Expertise<ul><li>Act as the senior technical writing authority; mentor junior writers informally and provide guidance on complex documentation challenges.</li><li>Promote knowledge sharing and best practices across the department; run documentation clinics and learning sessions.</li></ul>MEASURES OF SUCCESS<ul><li>Documentation completeness and quality: SDKs/modules and solution patterns meet standards; fewer gaps at release readiness.</li><li>Discoverability and adoption: Increased usage of indexed patterns and SDK documentation; positive feedback from consuming teams.</li><li>Operational alignment: Documentation standards consistently applied across Creation Centers; feedback loop drives continuous improvement.</li><li>Enablement impact: Intelligent tools and indexing reduce time to find relevant patterns and modules.</li></ul>QUALIFICATIONSBasic Qualifications<ul><li>5+ years in technical writing or documentation roles within software development environments.</li><li>Proven ability to define and enforce documentation standards and structures for complex technical artefacts.</li><li>Strong familiarity with software development lifecycle, APIs, SDKs, and integration patterns; ability to translate technical concepts into clear, actionable content.</li><li>Ability to interpret system diagrams (Data Flow, Sequence Diagrams) and translate them into clear documentation</li><li>Excellent communication and organizational skills; adept at managing multiple priorities and stakeholders.</li></ul>Preferred Qualifications<ul><li>Experience with AI/ML or GenAI projects; understanding of common patterns (RAG, agentic workflows, evaluation frameworks).</li><li>Hands-on experience with Confluence or similar knowledge management platforms; ability to design logical structures and indexing strategies.</li><li>Background in software engineering (as a developer or technical contributor) is a strong plus.</li><li>Familiarity with CI/CD pipelines, version control, and release documentation practices.</li><li>Experience with markup languages (Markdown) and API documentation standards (OpenAPI/Swagger) is a plus</li><li>Exposure to regulated environments (healthcare, pharma, finance) and audit-ready documentation standards.</li></ul>

Manager, Technical Writer 4949154

As Senior Manager, Software Engineer (Individual Contributor), you will be a senior technical authority within the Enterprise AI Capability Design Team, responsible for designing and implementing modular, reusable AI capabilities in the form of SDKs and associated components. Your mission is to build developer-first, extensible SDKs that enable internal teams and the broader community to integrate AI capabilities seamlessly into applications, while maintaining consistency, quality, and performance.You will design and implement core modules for RAG pipelines, agent and tool cookiecutters, MCP adaptors, and standardized interfaces that allow community-driven extensions. You will also collaborate on automated pipelines for code quality checks, benchmarking, and integration tests, ensuring every capability meets enterprise-grade standards for reliability, security, and observability.This role requires close collaboration with:<ul><li>Embedded AI Architecture Team and Solution Design Team to prioritize capabilities and define SDK interfaces aligned to real-world solution patterns.</li><li>AI Engineering Team, which actively develops PI increments of the SDK and integrates external technologies.</li><li>Digital Creation Centers and Forward Impact Teams (FITs), who will consume these capabilities in product delivery and provide feedback for continuous improvement.</li></ul>Why this Role MattersThis role is the engineering backbone of our AI capability strategy. By delivering high-quality SDKs and reusable modules, you enable rapid adoption, reduce bespoke engineering, and ensure parity between SDK and service implementations. Your work directly impacts developer experience, scalability, and the ability to operationalize AI capabilities across the enterprise.ROLE RESPONSIBILITIES1) SDK &amp; Module Development<ul><li>Design and implement modular SDK components for AI capabilities, including:<ul><li>RAG pipeline modules (parsers, chunkers, enrichers, retrievers, rankers, connectors)</li><li>Cookiecutters for agents and tools with MCP adaptors</li><li>Standardized interfaces for community-driven extensions</li></ul></li><li>Ensure SDKs are extensible, composable, and versioned with clear upgrade paths.</li></ul>2) Collaboration &amp; Interface Definition<ul><li>Work closely with Embedded AI Architects and Solution Designers to define SDK interfaces and design patterns based on prioritized use cases.</li><li>Partner with AI Engineering Team to integrate external technologies, validate new components, and align on PI increments for SDK evolution.</li><li>Engage with Digital Creation Centers and FITs to capture feedback on usability, performance, and integration experience.</li></ul>3) Code Quality &amp; Testing Automation<ul><li>Collaborate with Evaluation &amp; QA Engineers to integrate unit, integration, and performance tests into CI/CD pipelines.</li><li>Implement automated code quality checks, linting, and security scans for SDK components.</li><li>Contribute to benchmarking frameworks for new modules and features.</li></ul>4) Developer Experience &amp; Enablement<ul><li>Work closely with Developer Experience Engineers to deliver clear APIs, ergonomic SDK design, and developer-friendly documentation.</li><li>Provide quickstarts, code samples, and integration guides to accelerate adoption.</li><li>Participate in community enablement efforts, ensuring SDK interfaces are intuitive and extensible.</li></ul>5) Architecture &amp; Standards Alignment<ul><li>Align SDK design with enterprise architecture principles, ensuring parity with service implementations.</li><li>Collaborate with AI Capability Architecture Lead on interface contracts, observability hooks, and security-by-design patterns.</li><li>Maintain semantic versioning and backward compatibility policies for SDK releases.</li></ul>MEASURES OF SUCCESS<ul><li>SDK quality and adoption: High developer satisfaction; increased reuse across products; minimal bespoke implementations.</li><li>Release health: Predictable release cadence; automated quality gates; successful integration tests.</li><li>Performance and reliability: SDK modules meet latency, throughput, and cost targets; observability hooks implemented.</li><li>Community engagement: Positive feedback on extensibility and contribution workflows; active feature branch participation.</li><li>Documentation completeness: Clear quickstarts, code samples, and integration guides delivered with each release.</li></ul>QUALIFICATIONSBasic Qualifications<ul><li>7+ years in software engineering roles with strong experience in API/SDK design and modular architecture.</li><li>Proficiency in modern programming languages (Python required).</li><li>Hands-on experience with cloud-native development (Azure, AWS, or GCP) and containerized environments.</li><li>Strong understanding of CI/CD pipelines, automated testing, and code quality practices.</li><li>Ability to produce developer-friendly SDKs with clear documentation and ergonomic interfaces.</li><li>Proficiency in Git-based workflows (feature branches, PR reviews, semantic versioning)</li></ul>Preferred Qualifications<ul><li>Experience with AI/ML frameworks (PyTorch, TensorFlow) and GenAI/LLM ecosystems (prompt tooling, RAG, vector databases, agent frameworks).</li><li>Contribution to open-source projects and/or community-driven extensions.</li><li>Exposure to observability tooling (metrics, tracing, logging) and security-by-design principles.</li><li>Background in performance engineering and benchmarking for AI workloads.</li><li>Experience in regulated environments with audit-ready documentation and compliance standards.</li></ul>&nbsp;

Senior Manager, Software Engineer 4949163 

As part of Data &amp; AI practice, you will combine AI &amp; ML with data, analytics and automation under a bold strategic vision to transform business in a very pragmatic way, sparking digital metamorphoses. There will never be a typical day and you will continuously learn and grow. The opportunities to make a difference within exciting client initiatives are unlimited in the ever-changing technology landscape.You will lead multidisciplinary teams (onshore and offshore) to design and implement solutions using Python, PySpark, Databricks and modern cloud services, ensuring data flows are secure, reliable, performant, and well‑governed.Responsibilities:<ul><li>Lead and mentor a technical team (5–8 engineers) in designing and delivering data‑engineering solutions across cloud platforms (Azure/AWS/GCP).</li><li>Architect, develop, and optimize ETL/ELT pipelines using Python, PySpark, Databricks, and distributed processing frameworks.</li><li>Design and maintain scalable data architectures, including data lakes, lakehouses, warehouses, Delta tables, and semantic layers.</li><li>Establish best practices in data modelling (dimensional, wide‑table, data vault), data quality, metadata management, and data governance.</li><li>Collaborate with business owners, architects, product managers, and analytics teams to ensure end‑to‑end project delivery.</li><li>Define standards for data operations, monitoring, lineage, and CI/CD for data workloads.</li><li>Drive cloud platform adoption and modernization, ensuring solutions meet performance, cost‑efficiency, and compliance requirements.</li><li>Promote innovation by evaluating new data engineering tools, frameworks, and practices.</li><li>Mentor junior team members, providing guidelines to ensure high-quality deliverables.</li><li>Communicate complex technical solutions to senior management and diverse stakeholders effectively.</li><li>Contribute to sales activities through data and platform architecture expertise</li><li>Stay updated on industry trends and contribute to internal initiatives, R&amp;D, and business development projects.</li></ul>QualificationWHO WE’RE LOOKING FOR?<ul><li>BSc/MSc/PhD in Computer Science, Engineering, Information Systems, Data Management, or related field.</li><li>7+ years of experience in data engineering, with at least 2 years leading small technical teams.</li><li>Industry vendor certifications are desired (e.g. AWS, Azure, GCP, CNCF/Kubernetes or Databricks certifications); although not essential if you have demonstrable ability.</li><li>Strong hands‑on expertise with Python, PySpark, and Databricks (including Lakehouse &amp; Databricks Workflows).</li><li>Experience designing and deploying data solutions on Azure, AWS, or GCP.</li><li>Strong understanding of data modelling techniques, data lifecycle management, and enterprise data principles.</li><li>Expertise in designing, developing, and managing scalable, end-to-end data pipelines (ADF, Airflow or dbt,).</li><li>Proficient in Big Data Platforms (Hadoop, Databricks, Hive, Kafka, Apache Iceberg or Microsoft Fabric), Data Warehouses (Teradata, Snowflake, BigQuery etc.) and lakehouses (Delta Lake, Apache Hudi)</li><li>Proficient in programming languages such as SQL, Python and Pyspark with strong skills in writing scalable, readable and maintainable code using object-oriented programming concept.</li><li>Implement DevOps practices, including Git workflows and CI/CD pipelines (Azure DevOps, Jenkins, GitHub Actions) to enhance automation and streamline deployments.</li><li>Experience in project management frameworks such as Waterfall or Agile.</li><li>Solid experience with SQL and handling large datasets.</li><li>Familiarity with data governance frameworks (e.g., data quality controls, cataloging, lineage, roles &amp; policies).</li><li>Excellent communication skills—able to translate complex data concepts to both technical and non‑technical stakeholders.</li></ul>Considered a plus (not a prerequisite):<ul><li>Experience with Azure Data Factory, AWS Glue, or GCP Dataflow.</li><li>Experience with Microsoft Fabric solutions</li><li>Knowledge of Lakehouse architecture, Delta Live Tables, and Medallion design patterns.</li><li>Familiarity with CI/CD, DevOps, containerization (Docker/Kubernetes).</li><li>Exposure to data management tools (Purview, Collibra, Informatica, BigID, etc.).</li><li>Experience with different data execution paradigms, including low latency/streaming, batch, and micro-batch processing (Apache Kafka, Databricks Streaming processing, Apache StreamSets).</li><li>Knowledge of data management frameworks (data governance, data quality, data security, data dictionary, metadata management) with tools like Databricks Unity Catalog, Apache Atlas, Informatica etc.</li><li>Familiarity with containerization and orchestration tools like Docker and Kubernetes.</li><li>Understanding of API gateway and service mesh architectures (e.g., Istio).</li><li>Familiarity working with Linux-based operating systems.</li><li>Familiarity with working with REST APIs.</li><li>Experience in pre‑sales, solutioning, or client‑facing delivery leadership.</li><li>Fluency in English (verbal and written).</li><li>All male candidates should have fulfilled their military obligations.</li></ul>WHAT’S IN IT FOR YOU?<ul><li>Competitive salary and benefits, including but not limited to: life/health insurance, performance based bonuses, monthly vouchers, company car (depending on management level), flexible work arrangements, employee share purchase plan, TEA Accenture, parental leave and various corporate discounts</li><li>Continuous training &amp; development through global platforms &amp; local academy. At Accenture, we believe in bringing the best to our clients through continuous learning &amp; improvement – from basic skills to industry-specific content – available to all our people</li><li>Career coaching and mentorship to help you manage your career and develop professionally</li><li>Ongoing strength and skill-based evaluation process</li><li>Various opportunities to develop your career across a spectrum of clients, industries and projects</li><li>Diverse and inclusive culture</li><li>Opportunities to get involved in corporate citizenship initiatives, from volunteering to doing charity work</li></ul>

Job Ads Intern Μακεδονία, Information Technology / It - Page 4

staff qa engineer 4949292

Pfizer Hellas

techno functional manager - digital master data solutions 4948248

Pfizer Hellas

manager, software engineer 4949164

Pfizer Hellas

manager, technical writer 4949154

Pfizer Hellas

senior manager, software engineer 4949163

Pfizer Hellas

data engineering manager (data & ai)

Accenture

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