Data Platform Engineer -Payments & Transaction banking

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.