Data Analytics & Management, SrAssc
Design, build, and support trusted analytical data products forCore Reference Data,Security Master,IBOR Holdings & Transactions,DataHub,andStamford Data Warehouse migrationsand may other data initiatives.Developend-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
Developdata 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 inon-call/ major incident management, perform backfills where required, and support production stability for owned data products
Qualifications
Strong data engineering programming skills inPython, Java, and SQL
Solidprogramming skills inSpark & SQL, with hands-on knowledge of optimization and debugging
Hands-on experience withSnowflake andDatabricks as data platforms
Good understandingof open table formats such as Apache Iceberg, catalogs (any ofPolaris,Horizon,Unity), or metadata frameworks
Experience building production-grade services in cloud environments;AWS,GCPand/or Azure is preferred
Basic understanding of data modeling and data product concepts
Soliddebugging and performance-tuning skills for data pipelines
Financial services or enterprise data platform background is beneficial but not required
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