Job Title:Senior Data Engineer – Databricks & AWS
Location:Sunnyvale, CA
Duration: 11 Months + Extension
Bill Rate: $67/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Description

We are looking for an experienced Senior Data Engineer with strong expertise in Databricks, Python, PySpark, SQL, and cloud-based data engineering. The ideal candidate should have hands-on experience designing scalable data pipelines, implementing ETL/ELT processes, and building enterprise data platforms. Experience with AWS, Snowflake, data lakehouse architecture, and modern AI-powered data solutions is highly desirable.

Required Skills

8+ years of experience in Data Engineering.

Strong experience with Python, PySpark, and SQL.

Hands-on experience with Databricks and Delta Lake.

Strong knowledge of ETL/ELT pipeline development.

Experience with AWS services such as S3, Glue, Lambda, Redshift, and EMR.

Experience with Snowflake or other cloud data warehouses.

Strong knowledge of data modeling and data warehousing.

Experience with Apache Spark and distributed data processing.

Knowledge of data integration and orchestration tools.

Experience with Git, Jenkins, and CI/CD pipelines.

Strong understanding of data quality, performance tuning, and optimization.

Recent / Modern Skills (2025–2026)

Databricks Unity Catalog and data governance.

Databricks Lakehouse architecture.

Databricks Workflows and Delta Live Tables.

Databricks Genie and Genie Agents.

Model Context Protocol (MCP) integration.

Conversational analytics and Text-to-SQL.

AI-powered data engineering and automation.

Vector search and RAG integration with enterprise data.

Data pipeline observability and monitoring.

Infrastructure as Code using Terraform.

Responsibilities

Design, develop, and maintain scalable data engineering pipelines.

Build ETL/ELT workflows using Python, PySpark, and SQL.

Develop data processing solutions using Databricks and AWS.

Implement data lakehouse and data warehouse architectures.

Optimize Spark jobs and large-scale data processing workloads.

Implement data quality checks and data governance practices.

Integrate enterprise data sources into centralized data platforms.

Collaborate with architects, analysts, and business stakeholders.

Support production deployments, troubleshooting, and performance tuning.