aKube Inc
aKube Inc

Data Engineer - 1668

Santa Monica, CA, US • Posted 2 days ago • Updated 5 hours ago
Full Time
On-site
USD 92.50 per hour
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Employment Authorization
  • Analytical Skill
  • Apache Spark
  • Data Modeling
  • Extract
  • Transform
  • Load
  • ELT
  • PySpark
  • Scripting
  • Normalization
  • Issue Resolution
  • Workflow
  • SLA
  • Collaboration
  • Analytics
  • Reporting
  • Documentation
  • Data Engineering
  • Unity
  • Data Governance
  • Amazon Web Services
  • Amazon S3
  • Amazon EC2
  • Apache Airflow
  • Orchestration
  • JSON
  • Apache Parquet
  • Computerized System Validation
  • Git
  • GitHub
  • Continuous Integration
  • Continuous Delivery
  • DevOps
  • Migration
  • SQL
  • Snow Flake Schema
  • Databricks
  • Marketing
  • Acquisition
  • Advertising
  • Data Quality

Summary

Job 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: 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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: RTX1d3dc7
  • Position Id: c6f242c8f20b0f0cef210c5ffc875c60
  • Posted 2 days ago
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