Data Engineer, Senior

Build mission-critical data pipelines at Booz Allen Hamilton in McLean, VA (onsite), delivering scalable cloud-based data workflows that support analytics and enterprise applications. This Senior Data Engineer role combines hands-on engineering with Agile practices and strong platform ownership, backed by a comprehensive benefits package including health, retirement, and professional development support.

What you’ll do

• Design, develop, test, and maintain scalable data pipelines and data processing workflows.
• Build and optimize data solutions using Databricks, including data transformation, processing, and orchestration.
• Create automated workflows that improve reliability, efficiency, and repeatability of data processing.
• Integrate data from multiple sources and formats into enterprise data platforms.
• Develop and optimize data transformations using SQL and Python.
• Troubleshoot data pipeline issues, then identify opportunities to improve performance and reliability.
• Collaborate with software developers, data engineers, architects, and stakeholders to understand requirements and deliver technical solutions.
• Apply Agile development practices, version control, testing, and CI/CD across the software development lifecycle.

What you’ll need

• 5+ years of experience in data engineering, database development, or data warehousing.
• Strong SQL experience including complex queries, joins and aggregations, CTEs, window functions, query optimization, and performance tuning, plus relational database development, troubleshooting, and performance tuning.
• Experience developing and maintaining data pipelines using Python, and Apache Spark or PySpark for processing and transforming large datasets, including performance optimization and troubleshooting.
• Experience with Databricks as a data engineering platform.
• Experience with Amazon Web Services (AWS) and AWS data services such as S3, EMR, Glue, Lambda, or NoSQL databases.
• Experience with CI/CD pipelines, including using GitHub Actions for automated builds, testing, deployment, and environment promotion.
• Knowledge of database design principles and data warehousing concepts, including ETL/ELT, fact and dimension tables, star and snowflake schemas, slowly changing dimensions, data integration, and incremental data processing.
• Ability to independently troubleshoot pipeline failures, data issues, performance problems, and production incidents, including determining root causes and corrective actions.
• Public Trust and a Bachelor’s degree in Computer Science.
• U.S. citizenship is required.

Technologies you’ll work with

• Databricks, AWS (S3, EMR, Glue, Lambda), SQL, Python, Apache Spark, PySpark
• NoSQL databases
• GitHub Actions

Benefits

• Health, life, disability, financial, and retirement benefits
• Paid leave
• Professional development
• Tuition assistance
• Work-life programs
• Dependent care
• Recognition awards program

Nice to have

• Experience with data lake or Lakehouse architectures
• Experience with DevSecOps practices
• Experience working in federal or other highly regulated environments
• Knowledge of data modeling (conceptual, logical, and physical), dimensional modeling, and designing models for analytics and reporting
• Ability to contribute to technical design and architecture discussions, evaluate implementation approaches, and develop scalable, maintainable, reliable data engineering solutions
• Ability to use AI-assisted development tools for productivity, code generation and review, troubleshooting, SQL and Spark optimization, documentation, and accelerated learning while following organizational security and data-protection standards

Vetting and identity verification

• Selected applicants will be subject to a government investigation and may need to meet U.S. government eligibility requirements; Public Trust determination is required.
• During the hiring process, you will complete an identity verification process using advanced biometrics and AI to ensure authenticity and protect against identity fraud.
• You may be expected to be on camera during interviews and assessments, and the company may take your picture to verify identity and prevent fraud.

Compensation

Projected compensation range: $77,600 to $176,000 per year (annualized USD).

Work model

Onsite: Work will primarily be performed full-time at a customer facility, with collaboration directly with colleagues and customers as required by the role. Employees working virtually are generally expected to have cameras on during meetings.

Candidate AI usage policy

Use of artificial intelligence or other tools to assist with responses during interviews (in-person or virtual) is prohibited unless permission is explicitly provided.