Design, build, and support trusted analytical data products for Core Reference Data, Security Master, IBOR Holdings & Transactions, DataHub, and Stamford Data Warehouse migrationsand may other data initiatives. Develop 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

• Implement 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

• 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

• Develop 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

• 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

• Solid programming skills in Spark & SQL, with hands-on knowledge of optimization and debugging

• Hands-on experience with Snowflake and Databricks as data platforms

• Good understanding 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

• Basic understanding of data modeling and data product concepts

• Solid debugging and performance-tuning skills for data pipelines

• Financial services or enterprise data platform background is beneficial but not required