Senior Data Engineer (Secret Cleared)

CGI•Arlington, VA

1d•Onsite

About The Position

CGI Federal is seeking a Senior Data Engineer to design, implement, and support data pipelines and systems that meet client data needs within an Azure Data Lake/Databricks environment. This role requires an active Secret security clearance and will focus on new data ingest support for RFMS/C, RFMS/D, GovTA, and CashRec pipelines, enabling the current team to remain focused on GFACS and GFMS/RFMS priorities. This position is located in Arlington, VA.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (or equivalent practical experience).
5–8 years of experience in data engineering, ETL/ELT development, and production pipeline support.
Strong knowledge of ETL processes, data warehousing concepts, and data pipeline frameworks.
Proficiency in Python and SQL (data transformation, validation, and troubleshooting).
Hands on experience with cloud based data storage and processing solutions, preferably Azure and/or Databricks.
Experience implementing data validation/reconciliation practices and supporting data quality in production environments.
Ability to lead technical workstreams and coordinate across multiple teams and stakeholders.
Active Secret security clearance (required).

Nice To Haves

Experience with Apache Spark (including performance tuning) and Delta Lake (if applicable).
Experience with Azure services commonly used for ingestion/orchestration (e.g., ADLS, Azure Data Factory, Azure Synapse).
Experience with CI/CD and automated deployment approaches for data pipelines (tooling as applicable).
Experience working in Agile delivery environments.

Responsibilities

Design, build, and support scalable ingestion pipelines for RFMS/C, RFMS/D, GovTA, and CashRec into the Azure Data Lake/Databricks platform.
Implement ETL/ELT workflows (ingest, transform, validate, and publish) using Databricks and supporting Azure services as applicable.
Ensure data is accurate, consistent, and accessible for analytical and reporting use through automated validation, reconciliation, and data quality checks.
Optimize pipeline performance, cost, and reliability; troubleshoot failures, perform root cause analysis, and implement permanent fixes.
Collaborate with downstream consumers (analysts, reporting teams) and upstream source owners to define schemas, mappings, refresh cadence, and operational expectations.
Produce and maintain technical documentation, data mappings, operational runbooks, and support procedures.
Mentor junior data engineers and promote engineering best practices (coding standards, testing, peer reviews, and operational excellence).