Lead Data Engineer

Elder Research•Arlington, VA

23h•Hybrid

About The Position

As a Lead Data Engineer, you will support the Internal Revenue Services mission to combat tax fraud, identity theft, and non-compliance by designing and delivering secure, scalable, and automated data pipelines that power advanced analytics, machine learning models, and decision-support tools. You will work directly with IRS stakeholders, program managers, data scientists, and technical teams to translate complex business and compliance needs into reliable data engineering solutions. Your work will enable fraud detection, audit prioritization, refund review, and compliance risk analysis across large, sensitive tax and financial datasets. This role sits within an analytics-focused business unit supporting IRS enforcement, compliance, and research initiatives, partnering closely with data science and analytics teams to ensure data products are production-ready, trustworthy, and mission-aligned.

Requirements

7+ years of experience in data science, analytics, or a related technical field
Prior programming experience, preferably in Python, including data
Design, build, and deploy robust, repeatable, and automated data pipelines using Python, SQL to transform raw data into analytics and ML-ready datasets
Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, data engineering, business, or social sciences

Nice To Haves

Advanced degree (MS) in analytics, computer science, data science, mathematics, statistics, engineering, management information systems, decision science, or related fields
Experience with version control systems (Git, SVN, Mercurial) and collaborative programming practices (pair programming, code reviews)
Experience with containerization and environment management (venv, conda)
Experience with platforms and technologies such as Databricks and AWS
Prior Databricks experience in Unity Catalog, Pyspark, Spark SQL, and Jobs
Familiarity with vector, object, and document-based data storage systems
Experience implementing data engineering solutions in remote or austere environments, including use of bash and command-line tools
Experience with business intelligence and data visualization tools (Power BI, Tableau)
Understanding of the data analytics lifecycle (e.g., CRISP-DM) and how engineering supports downstream analytics and ML use cases

Responsibilities

Troubleshoot and resolve complex data and system issues across cross-functional and mission-critical environments with minimal supervision
Engineer solutions that integrate diverse data types, including transactional, financial, and textual data, to support compliance and fraud analytics
Collaborate with data scientists and stakeholders to deploy analytics applications, dashboards, and decision-support tools
Write, test, and refine reusable, well-documented code in Python, SQL, Java, and other languages using collaborative development practices
Build and maintain secure, scalable data pipelines and end-to-end systems, including operation within air-gapped or restricted government environments
Support the full engineering lifecycle, from concept and design through deployment, monitoring, and ongoing support
Produce technical documentation and deliver briefings or presentations to technical and non-technical audiences
Act as a technical consultant, translating business, compliance, and enforcement needs into effective data solutions
Engineer data pipelines that support fraud detection, compliance analytics, and predictive risk modeling across structured and unstructured data sources in Databricks
Develop and maintain end-to-end machine learning data workflows across on-premises and cloud environments, integrating backend systems with analytics platforms and user-facing applications
Partner closely with data scientists, analysts, product managers, and government stakeholders to align data engineering solutions with IRS mission objectives
Modernize and optimize data and ML workflows by implementing best practices for scalability, reliability, maintainability, and security
Translate client and stakeholder requirements into clear, actionable technical designs and implementation plans
Contribute effectively within agile, fast-paced development environments, supporting iterative delivery and continuous improvement
Demonstrate a strong willingness to learn new technologies, adapt to evolving requirements, and share knowledge across teams