Role Summary
The Data Platform Engineering Strategy Lead provides senior-level leadership to define, modernize and scale enterprise data platforms supporting Payments and Transaction Banking. The role blends platform architecture strategy with hands-on engineering oversight to enable high-volume, low-latency payments data use cases across clearing, settlement, liquidity and regulatory reporting. The position focuses on cloud-native data platforms, AI/ML enablement and governance-aligned delivery in a highly regulated, large-scale banking environment.

Key Responsibilities
1) Data Platform Strategy & Architecture
Define and own a multi-year data platform engineering roadmap aligned to Payments and Transaction Banking priorities (e.g., real-time payments, ACH, SWIFT, clearing and settlement).
Establish cloud-native and lakehouse-style reference architectures, balancing near-term delivery with long-term modernization and cost efficiency.
Translate architecture principles into pragmatic, implementable guidance for engineering and delivery teams.

2) Payments Data Modernization & Scale
Lead modernization of legacy data warehouses and integration layers into scalable, cloud-ready platforms supporting high-volume transactional data.
Enable ISO 20022-aligned data models, enriched payment event data and standardized integration patterns across batch and streaming use cases.
Support data quality, reconciliation and lineage requirements critical to payments operations and downstream risk, finance and regulatory reporting.

3) Emerging Technology & AI/ML Enablement
Assess and selectively adopt emerging data and analytics technologies (e.g., distributed query engines, open table formats, streaming frameworks, graph and NoSQL stores).
Evaluate AI/ML use cases for payments data (e.g., anomaly detection, fraud signals, liquidity forecasting) with focus on scalability, risk and value realization.
Define platform patterns for ML lifecycle management (MLOps) and secure integration into enterprise data platforms.

4) PoC-to-Production & Value Realization
Sponsor and govern proofs of concept, ensuring clear success criteria, engineering guardrails and alignment with enterprise standards.
Industrialize validated solutions into reusable accelerators, templates and patterns.
Quantify business impact and ROI to support prioritization and scaling decisions.

5) Governance, Risk & Compliance Alignment
Ensure alignment with enterprise data governance, metadata, lineage and data quality standards.
Embed regulatory and conduct-risk considerations (e.g., data privacy, auditability, model risk) into platform and solution design.
Promote responsible AI and controlled technology adoption in regulated payments environments.

6) Stakeholder Engagement & Enablement
Act as a trusted advisor to payments business leaders, technology teams and risk/compliance stakeholders.
Drive data literacy, best-practice adoption and engineering standards across distributed teams.
Influence platform investment and delivery decisions through clear articulation of trade-offs, costs and benefits.

Required / Must-have Skills & Experience
10+ years of experience in data engineering and/or data architecture within large-scale, cloud or hybrid environments.
Proven background in Payments or Transaction Banking data domains (e.g., real-time payments, ACH, SWIFT, clearing and settlement).
Hands-on expertise with modern data platforms and lakehouse architectures (e.g., Databricks, Snowflake) and cloud-native services.
Strong experience with streaming and event-driven data processing (e.g., Kafka) and ETL/ELT patterns.
Advanced proficiency in Python and SQL for data engineering, automation and analytics pipelines.
Solid understanding of AI/ML concepts, MLOps and integration of models into enterprise data platforms.
Practical knowledge of data governance, metadata, lineage and data quality tooling in regulated environments.
Demonstrated ability to translate architecture strategy into executable delivery patterns and influence senior stakeholders.
Experience operating in Agile / scaled-Agile delivery environments.

Preferred / Nice-to-Have Skills
Experience in financial services or other highly regulated industries.
Familiarity with distributed query engines, graph databases, NoSQL stores and open table formats (e.g., Iceberg, Delta Lake).
Exposure to data mesh concepts and domain-oriented data ownership models.
Cloud and platform certifications (AWS, Azure, GCP; Databricks, Snowflake, Oracle).
Demonstrated experience with FinOps and cloud cost optimization for data platforms.

Location: London Area, United Kingdom
Job Link: https://www.linkedin.com/jobs/view/4369473792