Accenture Federal Services is looking to solve the problem of improving the efficiency and reliability of their data engineering processes by building foundational frameworks for their data organization through automation and innovation.
Requirements
- Expert-level skills in Python and SQL
- Proven experience building and optimizing data pipelines on platforms like Databricks or Snowflake
- Deep understanding of data architecture, data modeling, or modern data warehousing concepts
- Experience with data orchestration tools like Airflow
- Hands-on expertise with Infrastructure-as-Code (Terraform) and CI/CD tools (e.g., GitLab CI, Jenkins)
- Familiarity with containerization technologies (Docker, Kubernetes)
Responsibilities
- Lead and mentor a team of automation-focused data engineers responsible for building and maintaining core data infrastructure frameworks
- Design and develop standardized, reusable ETL/ELT data pipeline templates for ingestion, transformation, and data product creation
- Architect and implement the CI/CD for Data (DataOps) strategy, enabling automated testing, validation, and deployment of data pipelines
- Integrate and manage automated data quality, testing, and monitoring tools (e.g., Great Expectations) directly into the core pipeline templates
- Develop and maintain a library of Infrastructure-as-Code (IaC) modules (e.g., Terraform) for provisioning common data engineering resources
- Collaborate with platform and data engineering teams to identify complex problems, troubleshoot bottlenecks, and engineer automated solutions
- Drive innovation by researching, prototyping, and implementing new technologies and patterns to improve the efficiency and reliability of our data engineering processes
Other
- Active TS or TS/SCI clearance
- Strong project management skills with experience in cross-functional initiatives
- Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
