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Senior Data Engineer

Spark Tek Inc

Job Description

Job Description

Location: San Jose, CA – Local Candidates Only
Job Type: Contract
Work Arrangement: Onsite – No Remote

Responsibilities
  • Lead end-to-end migration, transformation, and loading of high-volume, complex data into PostgreSQL.
  • Design, build, and maintain scalable ETL/ELT pipelines processing millions of records.
  • Map and reconcile complex legacy data structures across multiple business entities.
  • Develop data validation and reconciliation frameworks to ensure data integrity and completeness.
  • Optimize PostgreSQL performance through query tuning, indexing, partitioning, and batch/incremental loading.
  • Troubleshoot data quality issues, schema mismatches, and pipeline failures.
  • Document data mappings, transformation logic, data lineage, and migration runbooks.
  • Collaborate with business and technical stakeholders on migration requirements and timelines.
  • Establish monitoring, logging, and alerting for pipeline health and data quality.
  • Mentor junior data engineers and contribute to engineering best practices.
Required Qualifications
  • 7 years of experience in Data Engineering.
  • Strong to expert-level Python skills.
  • Deep hands-on experience with PostgreSQL and/or Oracle .
  • Proven experience designing and managing ETL/ELT pipelines at scale .
  • Experience with large-scale data migration projects involving millions of records.
  • Strong experience with data mapping, transformation, validation, and reconciliation.
  • Knowledge of data modeling, normalization, indexing, and partitioning.
  • Experience with Git and CI/CD practices.
  • Strong troubleshooting and problem-solving skills.
Preferred Qualifications
  • Experience with Airflow, Dagster, or Prefect .
  • Experience with AWS, GCP, or Azure .
  • Exposure to NoSQL and cross-database migrations.
  • Experience working in regulated or high-stakes data environments.
  • Experience with Docker and Infrastructure-as-Code .
  • Familiarity with Great Expectations, dbt tests , or similar data quality frameworks.
What Success Looks Like
  • Fully migrated and validated datasets in PostgreSQL with zero critical data loss or corruption.
  • Documented, repeatable, and optimized ETL/ELT pipelines.
  • Clear data lineage and audit trails for migration and validation activities.
Vacancy posted 15 days ago
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