Job Title: Engineering Leader – Data (Lloyds Technology Centre)
Location: Hyderabad, India (Hybrid: at least 2 days/week in office)
Experience: 1 5+ years
About Lloyds Technology Centre
Lloyds Technology Centre is the strategic technology hub for Lloyds Banking Group , enabling digital transformation and innovation across the banking ecosystem. Our mission is to build secure, scalable, and data-driven solutions that empower millions of customers and ensure compliance with financial regulations.
Own the delivery and operation of data products and platforms across multiple squads, with end ‑ to ‑ end accountability for outcomes, quality, and reliability. Establish reusable, metadata ‑ driven engineering patterns; elevate semantic models, KPI/metrics , and BI consumption ; and build an AI ‑ ready foundation (knowledge layers, knowledge graphs, semantic models) that accelerates analytics and machine learning use ‑ cases across the organisation.
Delivery ownership: Define roadmaps, OKRs, and release plans; oversee scope, estimation, risk, and stakeholder communication; ensure on ‑ time, on ‑ budget, quality delivery across squads.
Team ownership & leadership: Build and lead high ‑ performing teams (hiring, coaching, performance management); set coding standards, DoR /DoD, working agreements, and succession plans.
Engineering patterns at scale: Establish factory ‑ mode delivery via inner ‑ sourced, reusable frameworks (ingestion, transform, quality, lineage, observability) and golden paths with strong documentation
Semantic layer & metrics: Define and govern enterprise semantic models, KPI/metric definitions , conformed dimensions, metric stores, and query ‑ ready views to enable consistent BI and self ‑ serve analytics.
AI readiness & knowledge layers: Shape data for ML/GenAI—ontologies, knowledge graphs , feature/embedding strategies, and patterns that make the platform AI ‑ ready by design.
Data modelling & performance: Guide dimensional (star/snowflake) and Data Vault 2.0 modelling ; set standards for physical design on cloud warehouses (partitioning, clustering, caching, workload mgmt.)
Streaming & real ‑ time: Oversee event pipelines (Kafka/Pub/Sub/Kinesis + Flink/Spark) with exactly ‑ once semantics, replay, SLAs/SLOs, and resiliency patterns.
Quality, metadata & lineage: Make quality the default —data contracts, DQ rules/tests, reconciliation—and automate capture/propagation of technical/business metadata and end ‑ to ‑ end lineage
Security & compliance: Champion IAM, least ‑ privilege patterns, encryption, secrets mgmt., and auditability; embed privacy ‑ by ‑ design and regulatory controls into pipelines and platforms.
Vendor & partner management: Govern partner delivery (e.g., TCS/HCL), enforce standards, and ensure reusable IP is contributed back to inner ‑ source repos.
