enterprise ai & data architect for newra, part of accenture r00337981

ARE YOU READY to step into the New Era (NewRA) of AI-driven banking?

At Accenture Newra AI Hub, we are not just building technology, we are redefining how banking operates. As part of our strategic collaboration with Piraeus, Newra is designed to responsibly embed AI at the core of its business, moving beyond experimentation to real-world impact at scale. Built to make a real difference, Newra reflects our belief that AI creates value only when it genuinely improves people’s lives.

You will work on advanced AI solutions that span the full spectrum of the bank -from core banking systems to customer experience- simplifying complexity, automating critical processes and delivering measurable results where they matter most.

Joining Newra means becoming part of a high-performing team of innovators at the beginning of a major reinvention. This is a space for people who approach AI with depth, discipline and purpose. You will collaborate across disciplines, develop future-proof skills, and help turn technology into real-world transformation.

Every AI model is only as good as the data beneath it. As Enterprise AI & Data Architect, you will design the enterprise data and AI architecture backbone across the AI Hub, from target-state data models and governance standards to the semantic structures and model risk safeguards that let teams build faster, safer, and with greater confidence.  ​

What you’ll build

• Enterprise target-state data models (dimensional, data vault, star) and data structures supporting the AI Hub's analytics and ML use cases

• Integration/ingestion patterns, data access approaches, and reusable design standards, working with Data Engineers on implementation

• Data quality, governance, and compliance standards (profiling rules, validation, controls), including DORA resilience and EU AI Act readiness, embedded into Hub data designs

• Data cataloguing, taxonomy, and lineage practices so data assets are discoverable, well-described, and traceable across the platform

• Semantic models and knowledge-graph-ready structures connecting business concepts, data entities, and analytical use cases across the AI Hub

• Feature stores and data structures for high-quality ML training data, in collaboration with AI/ML Engineers and Data Scientists

• AI governance touchpoints in data designs: model risk, explainability, and auditability at the data layer

• Architecture reviews and technology/standard selection in alignment with the bank's enterprise architecture function

• Cost governance (FinOps) and capacity guardrails balancing scalability against total cost of ownership

• Data design reviews ensuring performance, cost, scalability, and reusability as Hub use cases and data volumes grow

Qualification

  What we need

• B.Sc. and/or M.Sc. in Computer Science, Information Systems, Engineering, or related field

• 6–8+ years in data engineering, data modelling, or enterprise data architecture, including 2–3 years as architect of record across multiple domains

• Enterprise architecture frameworks (TOGAF, ArchiMate, or Zachman), capability mapping, and target-state roadmapping

• Leading architecture review boards and technology/standard selection across teams

• Strong data modelling skills: conceptual, logical, physical, dimensional, data vault, star schema, ERD/UML, semantic modelling

• Knowledge of semantic data modelling concepts, including ontologies, taxonomies, knowledge graphs, and business glossaries, to support discoverability, interoperability, and AI-ready data products

• Advanced SQL/SparkSQL with a focus on schema and data modelling, hands-on with Databricks/Apache Spark and Azure Data Lake Storage (ADLS Gen2)

• Data warehouse and lakehouse architecture: integration/ingestion patterns and reusable design standards

• Data governance and compliance: GDPR, PSD2, DORA, EU AI Act, data quality frameworks, data residency and sovereignty

• AI model risk awareness and alignment with governance frameworks (e.g. watsonx.governance, MLflow)

• Data cataloguing, taxonomy, ontology, and lineage practices so data assets are discoverable, well-described, semantically connected, and traceable across the platform

• Stakeholder engagement: presenting trade-offs to CDO/CTO or steering committee level

• Strong collaborator across engineering, data science, and enterprise architecture teams

Nice to have:

• Streaming and batch processing patterns, feature store design for ML training data, metadata management, DevOps and version control (Git) for data design assets​

What's in it for you

• 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, parental leave and various corporate discounts

• Continuous training & development through global platforms & local academy. At Accenture, we believe in bringing the best to our clients through continuous learning & improvement – from basic skills to industry-specific content – available to all our people

• Career coaching and mentorship to help you manage your career and develop professionally

• Ongoing strength and skill-based evaluation process

• Various opportunities to develop your career across a spectrum of clients, industries and projects

• Diverse and inclusive culture

• Opportunities to get involved in corporate citizenship initiatives, from volunteering to doing charity work

• Under our Brain Regain initiative, extra relocation benefits may apply