Accenture Federal Services
Data Engineer
About this role
Accenture Federal Services seeks a Data Engineer to design and maintain data infrastructure supporting federal government missions. You'll build robust data pipelines, manage cloud-based systems, and integrate machine learning models while collaborating with cross-functional teams.
What you'll do
- Design and build scalable data pipelines using orchestration tools like Apache Airflow or dbt
- Extract, integrate, and manage data quality from multiple sources using SQL and cloud databases
- Process large-scale data workflows using Apache Spark, Databricks, or Kafka
- Deploy and scale machine learning models in production environments
- Monitor pipeline health and troubleshoot issues using CloudWatch, Datadog, or similar tools
- Collaborate with data scientists and analysts to understand requirements and document solutions
What they're looking for
- Python programming
- AWS cloud platforms (GCP or Azure also valued)
- SQL and relational/columnar databases (PostgreSQL, Snowflake, BigQuery)
- Data pipeline orchestration tools
- Apache Spark and big data frameworks
- MLOps and machine learning deployment
- Git, Jira, and Confluence
- Data monitoring and troubleshooting
Benefits
- Glassdoor Top 100 Best Place to Work
- Hands-on experience and professional development
- Industry certifications and training opportunities
- Collaborative and inclusive work environment
- Opportunity to support critical federal government missions
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Accenture Federal Services
Accenture Federal Services builds and maintains mission-critical technology solutions for the U.S. federal government, including cloud infrastructure, enterprise systems integrations, and cyber defense tools. The company is hiring DevOps engineers, full-stack developers, SAP specialists, and test engineers to support classified and unclassified federal government projects.
- Website
- accenturefederal.com
Likely interview questions
- Walk us through your experience designing and building data pipelines. What orchestration tools have you used, and how did you handle failures or delays?
- Describe a time you optimized a data workflow or query for performance. What was the bottleneck, and how did you resolve it?