Role Summary

Design, build, and support trusted analytical data products for Core Reference Data, Security Master, IBOR Holdings & Transactions, DataHub, 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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