Data Engineer

Job Title: Data Engineer

Location(s): Arlington, VA & Washington DC (DUE TO CUSTOMER REQUIREMENTS YOU MUST BE LOCATED IN THE GREATER WASHINGTON DC AREA)

Workplace: Hybrid

Clearance Required: Must have a IRS Public Trust w/a Full Background Investigation

Requisition Type: Pipeline – this is not a current opening but rather a talent pipeline for Data Engineers of all levels with an IRS Public Trust w/ background investigation interested in supporting the Government customer. When new IRS Data Engineer positions become available, this talent community will be the first place our recruiters look to fill the roles. Candidates with profiles in this talent community can also expect to receive regular updates on relevant new job opportunities. Be sure to also apply to any relevant current funded/awarded openings, if available.

Position Overview:

As a Data Engineer, you will work directly with clients, managers, and technical staff to understand business needs, develop technical plans, and deliver data-driven analytical solutions and tools that solve client problems. The Data Engineer will primarily create and deploy robust, repeatable, and automated data pipelines that will ingest and transform raw data to support various advanced analytics and machine learning models. The resulting pipeline should be developed to be deployed into existing large-scale applications or different data visualization platforms available.

This is also where you can add specific information about the BU that the position will be working within.

Position Requirements:

Required Clearance: Must have a IRS Public Trust w/a Full Background Investigation

Required Education: Undergraduate and/or graduate degrees in engineering, computer science, analytics, math, finance, accounting, management information systems, social sciences, physics, or decision science.

Required Skills / Experience:

• Design and deploy robust data engineering and ML pipelines using Python, R, and SQL to transform raw data into analytics-ready formats.
• Develop and maintain end-to-end ML solutions across on-premises and cloud environments, integrating backend systems with user-facing applications.
• Partner with data scientists, analysts, product managers, and client teams to align technical solutions with business objectives.
• Modernize and optimize ML workflows by implementing best practices that enhance performance, scalability, and maintainability.
• Thrive in agile, fast-paced environments by contributing to collaborative development cycles and iterative problem-solving.
• Translate client and stakeholder needs into actionable technical requirements through effective communication and engagement.
• Embrace continuous learning, willingly step outside comfort zones, and foster a knowledge-sharing culture within the team.
• Willing to travel and work on-site with clients as needed, adapting to varying project demands and team environments.

Essential Functions:

• Excel in troubleshooting and problem-solving across complex, cross-functional environments with minimal supervision.
• Leverage diverse data types—quantitative and textual—to drive informed, strategic decision-making.
• Partner with data scientists and stakeholders to design and deploy impactful data applications and visualizations.
• Write and refine reusable code in Python, SQL, Java, and other languages through collaborative peer reviews.
• Build and maintain secure, scalable data pipelines and end-to-end systems, including in air-gapped environments.
• Lead and contribute across the full engineering lifecycle, from concept through deployment and ongoing support.
• Produce technical documentation, manage infrastructure, and create tailored presentations for technical and non-technical audiences.
• Translate business needs into actionable data solutions through clear, consultative communication with clients and teams.

Preferred Skills and Qualifications:

• Advanced degree (MS) in a relevant field (analytics, math, statistics, computer science, management information systems, social sciences, engineering, physics, decision science, or business, etc.,)
• Experience using version control (e.g. git, svn, Mercurial) and collaborative programming techniques (e.g. pair programming, code reviews)
• Experience with containerization and environment management (venv, conda, etc.,)
• Experience with one or more technologies, such as R Shiny, Databric