AWS Data Engineer

Are you looking to take the next step in your career as an AWS Data Engineer? Let's chat and see if we are a good match!

Opportunity:

Virtual Service Operations is searching for an AWS Data Engineer to join our team. You’ll work with Python, PySpark, SQL, and AWS services like Glue, Redshift, and EMR to deliver secure, reliable, and well-modeled data solutions. Candidates should bring 5+ years of data engineering experience (3+ with AWS).

Key Responsibilities:

• Design, develop, and maintain ETL/ELT pipelines that ingest, transform, and load structured and semi-structured data from multiple sources.
• Build and optimize data solutions using AWS services such as Glue, S3, Lambda, Redshift, EMR, Athena, and Step Functions.
• Develop data processing jobs in Python, PySpark, and SQL, with a focus on performance, reliability, and maintainability.
• Model data for data lakes and data warehouses, including schema design, partitioning, and cataloging.
• Implement data quality checks, monitoring, logging, and alerting for production pipelines.
• Apply security and compliance best practices (IAM, encryption, least privilege) appropriate for a federal environment.
  Support CI/CD and infrastructure-as-code practices for data workloads.
• Collaborate with stakeholders to gather requirements, troubleshoot issues, and document solutions.

Required Qualifications:

• Strong proficiency in Python and SQL; experience with PySpark/Spark.
• Experience building and supporting production ETL pipelines with large datasets.
• Solid understanding of data warehousing concepts, dimensional modeling, and data lake architecture.
• Experience with version control (Git) and Agile delivery.
• Strong communication skills and ability to work with technical and non-technical stakeholders.

Preferred Qualifications:

• AWS certifications (Data Engineer – Associate, Solutions Architect, or similar).
• Experience with Terraform or CloudFormation.
• Experience with orchestration tools such as Airflow (MWAA) or Step Functions.
• Familiarity with Databricks, Snowflake, or Informatica.
• Experience with streaming tools (Kinesis, Kafka).
• Prior federal government experience, ideally with financial regulatory data (e.g., SEC, FINRA, Treasury, Federal Reserve).
• Experience working in FedRAMP or other compliance-driven environments.