Data Platform Engineering Strategy Lead – Payments & Transaction Bankin

Data Platform Engineering Strategy Lead Payments & Transaction Bankin
Hybrid 2/3 days a week in office
Visa: any but no OPT CPT

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.