Senior Data Engineer
Investment Management
SeniorDataEngineer-Manager
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer - Manager at Senior Data Engineer. Skills: Data Platform Engineering, Data Pipeline Engineering, Cloud Data Platforms, Apache Spark, Snowflake, Databricks. Building and operating modern data platform and data pipeline capabilities. Building and operating end-to-end batch and streaming data pipelines on lakehouse platforms”
What You'll Achieve.
Reliable delivery; Day-to-day operations; Continuous improvement of production data workloads; Trusted, auditable, production-grade outcomes
Industry & Context.
Troubleshoot data and environment issues; Impact analysis; Root-cause analysis
What They're Looking For.
Must Have
10+ years of experience in software and/or data, BS/MS in Computer Science, Engineering, or equivalent practical experience, programming skills in Python and Java/Scala, ability to write production-quality, well-tested, maintainable code, Hands-on experience with Apache Spark (batch and/or streaming), including troubleshooting, performance tuning, and cost-aware optimization, Hands-on experience with Snowflake and Databricks in an enterprise environment, including job orchestration, query optimization, and platform operations, Experience building production-grade data pipelines in cloud environments (AWS GCP or Azure acceptable), Solid understanding of data modeling concepts, ETL/ELT patterns, schema evolution, and distributed data processing principles across structured and unstructured data, Working knowledge of lakehouse/data platform components (e. g. , Databricks, Snowflake, Trino, Iceberg) and interoperability, connectivity, and access patterns, Working knowledge of open table formats and metadata/catalog ecosystems (e. g. , Apache Unity Catalog or equivalent), Experience in Capital Markets, with understanding of key reference/application domains (e. g. , Securities, Benchmarking, Holdings, Sales, Accounts, Products)
Nice to Have
Experience with data quality frameworks, automated reconciliation patterns, and data lineage tooling, Professional Certificates on Snowflake and/or Databricks, Professional Certificates on AWS, GCP and/or Azure
What You'll Do.
Building and operating modern data platform and data pipeline capabilities
Building and operating end-to-end batch and streaming data pipelines on lakehouse platforms
analytics-ready datasets aligned to established standards
Developing production-grade pipeline code using Python
Implementing and maintaining Apache Iceberg tables
Delivering governed data integration and curation for reference and application publish semantic/serving models
Implementing automated data quality
Maintaining data product interfaces (schemas
and lifecycle processes)
Troubleshooting data and environment issues
Supporting incident management
Maintaining production stability for data pipelines
How You'll Work.
Team & Collaboration
Partner with domain SMEs, architects, and application teams to understand data requirements and semantics; Collaborate with global engineering, operations, and business stakeholders
Communication Scope
Excellent written and verbal communication skills
Full Job Description
**Officer – Data Platform Engineering** **Role Summary** State Street Investment Management’s Data, Analytics & AI Services (DAAIS) team is seeking a Senior Data Engineer (Officer) for our Bangalore, India office. This role is accountable for **building and operating** modern data platform and data pipeline capabilities that enable the investment management business. The successful candidate will bring strong hands-on expertise in distributed data processing, data pipeline engineering, and cloud-based data platforms. The role will execute on solutions and technical direction defined by platform leadership/architecture, with a focus on reliable delivery, day-to-day operations, and continuous improvement of production data workloads. **Key Responsibilities** * Build and operate end-to-end batch and streaming data pipelines on lakehouse platforms (Snowflake/Databricks, Apache Spark), delivering curated, analytics-ready datasets aligned to established standards. * Develop production-grade pipeline code using Python, Java/Scala, and SQL, applying best practices for reliability, maintainability, performance, and testability. * Implement and maintain Apache Iceberg tables based on defined patterns, including partitioning, schema evolution, and metadata/catalog practices to support efficient analytical access. * Deliver governed data integration and curation for reference and application domains; publish semantic/serving models via BI tools and other approved consumption interfaces. * Implement automated data quality, validation, reconciliation, lineage, and observability to ensure trusted, auditable, production-grade outcomes. * Partner with domain SMEs, architects, and application teams to understand data requirements and semantics, and implement agreed logical/physical models and consumption contracts. * Use standard CI/CD pipelines and platform tooling to deploy and run data workloads; follow platform guardrails, security requirements, and operational best practices. *
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