Role Summary

Design, build, and support trusted analytical data products for Core Reference Data,Security Master,IBOR Holdings & Transactions,Data Hub,Stamford Data Warehouse and may other data initiatives. Own end-to-end batch/stream pipelines, logical and physical data models, and production-grade curation of reference and application data using open Lakehouse technologies, in partnership with domain SMEs, architects, and platform teams.

Key Responsibilities

• Build and operate scalable batch and streaming pipelines using the Snowflake and/or Databricks tech stack, Spark, or Informatica ETL (where reused) to deliver analytics-ready datasets
• Design, implement, and optimize Iceberg-based tables, partitions, and metadata structures for consistent and performant analytical access
• Implement processing, storage, and delivery for reference and application data domains including security data and IBOR holdings/transactions, integrating source feeds (RKS, PORTIA, Aladdin, CRD Cloud) into curated analytical datasets
• Partner with domain SMEs to define data semantics (Account, Product, Holdings, Transactions, Benchmarks, etc.), stewardship expectations, and consumption contracts (data exchange interfaces and consumption APIs)
• Implement automated data validation, data quality rules, reconciliation checks, and lineage capture to ensure trusted analytical outcomes
• Build and publish curated semantic layer data models (serving models, marts) and expose them via governed BI endpoints and/or consumption APIs, ensuring consistent metrics and business definitions
• Define and manage data product interfaces for consumption (schemas, SLAs, documentation, versioning and backward compatibility) to support stable API and BI integrations
• Work with Platform Engineering to standardize deployment via CI/CD, productionize jobs, and adopt platform guardrails and observability patterns
• Collaborate with architects and application teams to define data strategies and deliver logical/physical data models aligned to analytical workloads
• Optimize pipeline and query performance through appropriate database partitioning, archiving, and purging patterns
• Participate in on-call / major incident management, perform backfills where required, and support production stability for owned data products

Qualifications

• Strong data engineering programming skills in Python, Java, and SQL
• Strong programming skills in Spark & SQL, with hands-on knowledge of optimization and debugging
• Hands-on experience with Snowflake and Databricks as data platforms
• Working knowledge of open table formats such as Apache Iceberg, catalogs (any of Polaris,Horizon,Unity), or metadata frameworks
• Experience building production-grade services in cloud environments; AWS,GCP and/or Azure is preferred
• Experience with structured and unstructured ingestion, schema evolution, and data modeling
• Strong debugging and performance-tuning skills for data pipelines
• Experience designing curated analytical/semantic data models for consumption (BI/metrics layers and/or serving models), including governance, documentation, and change management
• Working knowledge of building data products for consumption via APIs and BI endpoints, including interface contracts, performance considerations, and access controls

About State Street

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We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

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