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aKube Inc

Data Engineer - 1668

aKube Inc Santa Monica, California, United States · $192K/yr

Information Technology & Services · 51-200 employees

3 d ago
data-engineer Mid (2-5 yrs) Contractor United States
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About the role

Develop and maintain ETL/ELT pipelines using Databricks, PySpark, and SQL to support marketing acquisition reporting. Manage data ingestion, normalization, validation, and ensure pipeline reliability through monitoring and performance optimization.

What they look for

Data Engineering SQL PySpark Spark SQL Databricks Delta Lake Unity Catalog ETL/ELT Pipelines Data Modeling Distributed Processing AWS Apache Airflow Git CI/CD DevOps

Requirements

Requires at least 4 years of professional data engineering experience with strong proficiency in SQL, PySpark, and Databricks. Candidates must have experience with cloud services, data modeling, and modern DevOps practices.

Full description

City: Santa Monica, CA/ Burbank, CA or Glendale, CA

Onsite / Hybrid / Remote: Hybrid. Onsite Monday through Thursday. Remote Friday.

Duration: 6 months - Contract to Hire

Rate Range: Up to $92.50/hr on w2

Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B

Must Have:

  • 4+ years of Data Engineering experience.
  • Strong SQL skills, including complex joins, window functions, and analytical queries.
  • Hands-on production experience with PySpark and/or Spark SQL.
  • Databricks experience, including Delta Lake and Unity Catalog.
  • Strong understanding of ETL/ELT pipelines, data modeling, and distributed processing.

Responsibilities:

  • Develop and maintain ETL/ELT pipelines using Databricks, PySpark, and SQL for marketing acquisition reporting.
  • Migrate existing Snowflake SQL scripts and transformations into Databricks Unity Catalog.
  • Build ingestion pipelines for external marketing vendor data.
  • Perform data parsing, normalization, transformation, and validation.
  • Implement data quality checks, monitoring alerts, and issue resolution workflows.
  • Troubleshoot pipeline failures and improve processing performance.
  • Support SLA monitoring, logging improvements, and pipeline reliability.
  • Collaborate with analytics teams to ensure accurate reporting datasets.
  • Participate in code reviews, documentation, and engineering best practices.

Qualifications:

  • Minimum 4 years of professional Data Engineering experience or equivalent.
  • Experience building production pipelines in Databricks.
  • Strong understanding of Delta Lake, Unity Catalog, Lakehouse architecture, and data governance.
  • Experience with AWS services such as S3, IAM, EC2, Glue, or Lambda.
  • Experience with Apache Airflow or similar orchestration tools.
  • Experience processing JSON, Parquet, CSV, and other semi-structured data.
  • Experience with Git/GitHub, CI/CD, and modern DevOps practices.

Nice to Have:

  • Experience migrating SQL transformations from Snowflake to Databricks.
  • Experience working with marketing acquisition or advertising data.
  • Experience implementing data quality monitoring and pipeline reliability improvements.

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