State Street
DataEngineer,SeniorAssociate
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer, Senior Associate at State Street. Skills: Data Engineering, Python, Java, SQL, Spark, Snowflake, Databricks, Data Modeling, Data Pipelines. Design, build, and support trusted analytical data products. Develop end-to-end batch/stream pipelines”
What You'll Achieve.
trusted analytical data products; analytics-ready datasets; consistent and performant analytical access; curated analytical datasets; trusted analytical outcomes; consistent metrics and business definitions; stable API and BI integrations
Industry & Context.
optimization; debugging; performance-tuning
on-call / major incident management
What They're Looking For.
Must Have
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, Basic understanding of data modeling and data product concepts, Solid debugging and performance-tuning skills for data pipelines
Nice to Have
Experience building production-grade services in cloud AWS, GCP and/or Azure is preferred, Financial services or enterprise data platform background is beneficial but not required
What You'll Do.
and support trusted analytical data products
Develop end-to-end batch/stream pipelines
Develop logical and physical data models
Develop production-grade curation of reference and application data
Build and operate scalable batch and streaming pipelines
Implement Iceberg-based tables
and metadata structures
and delivery for reference and application data domains
Integrate source feeds into curated analytical datasets
Implement automated data validation
reconciliation checks
Build and publish curated semantic layer data models
Expose data models via governed BI endpoints and/or consumption APIs
Develop data product interfaces for consumption
Standardize deployment via CI/CD
Adopt platform guardrails and observability patterns
Define data strategies
Deliver logical/physical data models aligned to analytical workloads
Participate in on-call / major incident management
Perform backfills where required
Support production stability for owned data products
How You'll Work.
Team & Collaboration
in partnership with domain SMEs, architects, and platform teams; Collaborate with architects and application teams
Full Job Description
* 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. 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
Applying for this Data Engineer, Senior Associate role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Workday
- Workday has a multi-step form — save your progress after every section.
- "Apply With LinkedIn" can fail or lose data; manual entry is more reliable.
- Watch for the "Submit for Review" final step — hitting "Save" alone does not submit.
- Job requisition numbers are useful when following up with HR by email.
ANONYMOUS · UNFILTERED
What do employees actually say about State Street?
Real rants from real employees. Read before you apply.