HR Data Engineer/Data Scientist

ICF•Reston, VA

7d•$131,256 - $223,134•Remote

About The Position

ICF is seeking a highly capable HR Data Engineer / Data Scientist to support a large federal client in modernizing its enterprise learning and workforce data ecosystem. This role centers on designing and sustaining a scalable data integration capability that unifies multiple workforce - related data sources into a consistent, validated structure suitable for downstream HR and training systems. This position requires expertise in enterprise data engineering, workflow automation, data quality frameworks, metadata governance, and the translation of complex business rules into reliable technical logic. The ideal candidate brings experience working with HR, identity, or workforce - related datasets in large organizations and can partner directly with senior stakeholders across technical and business domains. Prior experience working in federally- regulated environments and proximity to the DC-Maryland-Virginia (DMV) area with the ability to go onsite as needed is strongly preferred.

Requirements

Bachelor’s degree in a related field (e.g., Data Science, Computer Science, Engineering, Mathematics, Information Systems, HR Analytics, or similar).
8+ years of professional experience in data engineering, data science, analytics, or a closely related field.
Extensive experience sourcing, integrating, transforming, and validating structured and unstructured data from diverse systems (e.g., developing Spark jobs, performing data manipulation with PySpark /pandas, and authoring robust, production‑grade SQL transformations ) .
Proven ability to design and implement automated data workflows, integration pipelines, and large ‑ scale transformation logic (e.g., orchestrating pipelines using Apache Airflow, Azure Data Factory (ADF), or AWS Glue) .
Hands-on experience developing predictive models, statistical analyses, or machine learning solutions (e.g., building and validating models in Python using PySpark ML, pandas, or related libraries) .
Strong understanding of data quality management, validation frameworks, error ‑ handling approaches, data governance, master data management, and enterprise metadata practices (e.g., MDM survivorship rules, identity resolution logic, validation utilizing Great Expectations) .
Demonstrated ability to facilitate requirements gathering, define business rules, and translate stakeholder needs into clear technical specifications (e.g., documenting required i mport fields and formats, converting business logic into ETL/ELT transformations) .
Excellent communication skills with the ability to clearly convey complex technical concepts to non ‑ technical or cross ‑ functional audiences (e.g., explaining data lineage, logging, and metrics practices used to ensure pipeline traceability) .
Exceptional documentation abilities and attention to detail (e.g., version-controlled artifacts in Git, CI/CD deployment documentation, and test suites using pytest and Great Expectations) .

Nice To Haves

Master’s degree preferred .
5+ years of experience supporting large ‑ scale or complex data ecosystems, preferably in government, healthcare, or highly regulated domains.
Experience supporting HR import processes for enterprise systems (e.g., HRIS, LMS, identity systems).
Background working with federal clients or within environments that require strict attention to data standards, compliance, and documentation.

Responsibilities

Design, build, and maintain automated pipelines that unify multiple HR and workforce ‑ related datasets into standardized, validated outputs.
Develop and document transformation rules, business logic, mapping structures, and data quality checks to ensure accuracy and consistency.
Create scalable workflows for ingesting structured and semi ‑ structured data from diverse systems with varying formats and update cycles.
Implement validation routines, anomaly detection, and error ‑ handling mechanisms to maintain reliable, high ‑ quality data products.
Produce well ‑ structured, standardized outputs compatible with enterprise learning or workforce management platforms.
Collaborate with technical and non ‑ technical stakeholders to refine requirements, resolve data ambiguities, and support decision ‑ making.
Maintain alignment with enterprise ‑ wide data standards, schemas, and governance guidance as they evolve.
Participate in cross ‑ team or cross ‑ agency coordination forums to represent program needs and communicate the impact of data ‑ related decisions.
Develop and execute test plans, including business ‑ rule validation, regression checks, and iterative QA cycles.
Identify process improvements, integration gaps, and opportunities to enhance automation, scalability, and sustainability.
Ensure solutions continue functioning amid changes to enterprise systems, dependencies, or data availability.
Prepare and deliver technical documentation, operational procedures, and knowledge ‑ transfer materials for long ‑ term maintainability.
Communicate risks, dependencies, and technical impacts clearly and proactively to project managers, leadership, and governance groups.