Data Engineer

Responsibilities

• Design, develop, and maintain data pipelines leveraging Python, Spark/PySpark, and cloud-native services.
• Build and optimize data workflows, ETL processes, and transformations for large-scale structured and semi-structured datasets.
• Write advanced and efficient SQL queries against Snowflake, including joins, window functions, and performance tuning.
• Develop backend and automation tools using Golang and/or Python as needed.
• Implement scalable, secure, and high-quality data solutions across AWS services such as S3, Lambda, Glue, Step Functions, EMR, and Cloud Watch.
• Troubleshoot complex production data issues, including pipeline failures, data quality gaps, and cloud environment challenges.
• Perform root‑cause analysis and implement automation to prevent recurring issues.
• Collaborate with data scientists, analysts, platform engineers, and product teams to enable reliable, high‑quality data access.
• Ensure compliance with enterprise governance, data quality, and cloud security standards.
• Participate in Agile ceremonies, code reviews, and Dev Ops practices to ensure high engineering quality.

• Skills‑Data Engineer:
  Python, Spark/PySpark, AWS, Golang, complex Snowflake SQL, Java/Python, AWS (Glue, EC2, Lambda).
• Proficiency in Python with experience building scalable data pipelines or ETL processes.
• Strong hands‑on experience with Spark/PySpark for distributed data processing.
• Experience writing complex SQL queries (Snowflake preferred), including optimization and performance tuning.
• Working knowledge of AWS cloud services used in data engineering (S3, Glue, Lambda, EMR, Step Functions, Cloud Watch, IAM).
• Experience with Golang for scripting, backend services, or performance‑critical processes.
• Strong debugging, troubleshooting, and analytical skills across cloud and data ecosystems.
• Familiarity with CI/CD workflows, Git, and automated testing.

• Mid‑Senior level

• Contract

• Information Technology

• Staffing and Recruiting and Financial Services