Data Platform Engineering Strategy Lead - Payments & Transaction Banking
2 days ago
City of London
Crisil Integral IQ delivers solutions and analytics to top financial institutions, driving strategic transformation, risk optimization, and operational excellence. Our offerings across research, risk, lending, analytics and operations have empowered clients to navigate complex markets, mitigate risks and unlock new opportunities. Our domain expertise, innovative solutions, and future-ready technologies such as AI and data science give clients the confidence to accelerate growth and achieve sustainable competitive advantage. Our globally diverse workforce operates in the Americas, Asia-Pacific, Europe, Australia and the Middle East. 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. Skills required: • 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. 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