Vista previa de la oferta
Senior Data Engineer - Senior Manager
senior · Technology / Software Development
Role: Senior Developer – Modern Data Platform Architect
Role Summary
We are seeking a highly skilled Senior Developer / Data Platform Engineer to design and build scalable data pipelines and modern Lakehouse solutions on Databricks-based platforms. The candidate should have strong expertise in PySpark, ETL/ELT frameworks, Data Quality, Apache Iceberg, and job orchestration, with exposure to asset management domain considered a strong advantage.
Key Responsibilities
1. Data Platform Development & Engineering
- Design and develop scalable data ingestion and transformation pipelines using PySpark and SQL.
- Build and manage batch and streaming ETL/ELT pipelines on Databricks.
- Implement data processing frameworks supporting high-volume enterprise datasets.
2. Lakehouse Architecture & Iceberg Implementation
- Design and maintain modern data Lakehouse architecture (Bronze/Silver/Gold layers).
- Work on Apache Iceberg / open table formats including schema evolution, partitioning, and compaction strategies.
- Ensure interoperability across engines and optimize data storage formats for performance.
3. Data Ingestion & Pipeline Management
- Design and monitor end-to-end ingestion pipelines (batch + near real-time).
- Build reusable ingestion frameworks for structured and semi-structured data.
- Manage dependencies, data lineage, and pipeline orchestration workflows.
4. Data Transformation & dbt (Nice to Have)
- Implement transformation logic using dbt or similar ELT frameworks.
- Standardize transformation pipelines for maintainability and versioning.
5. Data Quality & Observability
- Implement robust data quality checks, validation rules, and reconciliation processes.
- Develop monitoring frameworks for pipeline failures, SLA tracking, and alerting.
- Ensure data governance and audit compliance across datasets.
6. Job Orchestration & Workflow Management
- Implement and manage orchestration tools (Airflow / Databricks Jobs / other schedulers).
- Handle pipeline scheduling, retries, dependency chaining, and failure recovery.
7. CI/CD & DevOps Enablement
- Build and maintain CI/CD pipelines for data workflows and infrastructure.
- Automate deployment of Databricks jobs, notebooks, and configurations.
- Manage code versioning, release management, and environment promotion.
8. Performance Optimization
- Optimize Spark jobs for cost, performance, and scalability.
- Tune queries, partitioning strategies, and resource utilization.
9. Collaboration & Stakeholder Engagement
- Work with business teams, data architects, and upstream/downstream systems.
- Translate business requirements (especially in asset management domain) into technical solutions.
- Support data modeling (Data Vault / Star Schema exposure is a plus).
10. Platform Governance & Best Practices
- Follow enterprise standards for data security, access control, and governance.
- Implement Unity Catalog / RBAC / metadata management best practices.
- Ensure compliance with platform architecture and operational standards.
Required Skills
Must Have
- Strong experience in Databricks and PySpark
- ETL/ELT pipeline design and development
- Data ingestion and pipeline orchestration
- SQL and distributed data processing
- Data quality and validation frameworks
- Experience with Apache Iceberg / open table formats
- Cloud experience (AWS preferred)
Good to Have
- Exposure to Asset Management / Financial Data domain
- dbt (Data Build Tool)
- Data modeling (Data Vault / Star Schema)
- Knowledge of Snowflake / Unity Catalog
- Experience with CI/CD (Terraform, Git-based deployments)
About State Street
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