Data Engineering Senior Manager

Booz Allen Hamilton•McLean, VA

4d•Onsite

About The Position

This is a people leadership role that also requires strong technical judgment. You’ll set direction, align priorities, remove delivery barriers, develop talent, and create conditions for the team to operate with speed and autonomy. Rather than prescribing every implementation detail, you’ll provide clear context, establish engineering standards, and empower experienced engineers to make sound technical decisions and take ownership of outcomes. You’ll support a high-visibility, business-critical analytics program for a Fortune 500 company. Working closely with product owners, architects, analysts, data scientists, developers, and data consumers, you’ll translate business priorities into an actionable technical roadmap and ensure the team delivers reliable, scalable, and maintainable data products. You’ll stay close enough to the architecture and engineering work to guide design decisions, challenge approaches, review technical tradeoffs, and help resolve complex issues. At the same time, your primary impact will come from leading the team, improving delivery predictability and strengthening the systems and practices around the engineers. You’ll have the autonomy to advance the team’s future way of working, including evolving its use of Databricks, establishing reusable pipeline patterns, expanding automated testing and CI / CD, improving data quality and observability, strengthening production operations, and introducing AI-assisted engineering practices responsibly. Your leadership will help the team move beyond delivering individual pipelines toward operating a scalable, product-oriented data platform. Due to the nature of work performed within this facility, U.S. citizenship is required.

Requirements

10+ years of experience in data engineering, data science, data platform development, or enterprise data delivery
Experience managing or leading teams of experienced technical professionals, including coaching, performance feedback, work prioritization, and delivery accountability, and providing technical leadership for complex, enterprise-scale data platforms and data products
Experience developing and operating data solutions using Databricks and Apache Spark
Experience designing and maintaining scalable ETL and ELT pipelines, including curated or gold-layer data products
Experience with data modeling, data warehousing, and the processing of structured and unstructured data
Experience with Python and SQL
Experience with software engineering and DataOps practices, including source control, code review, automated testing, CI / CD, monitoring, and production support
Ability to balance delivery speed with data quality, reliability, governance, security, and long-term maintainability
Ability to communicate technical concepts, risks, priorities, and tradeoffs to engineering teams, business stakeholders, and senior leaders
HS diploma or GED

Nice To Haves

Experience with Databricks capabilities such as Delta Lake, Unity Catalog, workflow orchestration, performance optimization, and platform governance
Experience implementing medallion architecture and managing data products across bronze, silver, and gold layers
Experience modernizing a data engineering team’s development practices, operating model, or platform architecture
Experience building reusable data engineering frameworks, shared services, or self-service platform capabilities
Experience with data observability, lineage, metadata management, and automated data quality controls
Experience supporting analytics, machine learning, DataOps, or MLOps use cases
Experience introducing AI-assisted software engineering tools and practices with appropriate quality, security, and governance controls
Experience evaluating generative AI or agentic workflow capabilities for enterprise engineering use cases
Experience working in an Agile delivery environment
Possession of excellent leadership, facilitation, written communication, and executive stakeholder management skills

Responsibilities

Lead, coach, and develop a team of five highly experienced data engineers, including setting goals, providing feedback, supporting career development, and fostering a culture of accountability and continuous improvement.
Own the team’s delivery commitments, technical roadmap, operating rhythm, and stakeholder communications.
Provide technical direction for data pipelines and enterprise data products developed in Databricks.
Empower engineers to operate autonomously while maintaining clear standards for architecture, code quality, testing, documentation, deployment, security, and production support.
Guide architecture and design decisions, evaluate technical tradeoffs and help the team resolve complex engineering and operational challenges.
Establish reusable engineering patterns and automation that improve delivery speed, reliability, maintainability, and scalability.
Advance modern DataOps practices, including CI / CD, automated testing, data quality monitoring, observability, release management, and incident response.
Partner with business and technical stakeholders to prioritize work, manage dependencies, communicate risks, and translate strategic objectives into executable plans.
Evaluate and introduce new technologies and engineering practices that improve team effectiveness and create measurable business value.