Data Engineer

ICF • Reston, AL

20d • $81,499 - $138,549 • Remote

About The Position

ICF's Digital Modernization Division is an information technology and management consulting organization that delivers integrated, strategic solutions to federal clients. We bring expertise in cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation to support mission-critical government programs. Join a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. This cloud-based platform provides data storage, analytics, governance, and AI/ML capabilities that enable thousands of users to transform data into actionable insights. As demand continues to grow, the team is focused on migrating legacy workloads, streamlining onboarding and support, expanding platform capabilities, and helping organizations across the enterprise adopt modern data and AI solutions at scale. The Data Engineer builds and maintains data pipelines, transformations, semantic views, conformed dimensions, and consumption-ready data models for analytics, reporting, and AI/ML use cases. Implements pipeline documentation, lineage support, release-readiness evidence, quality checks, and defect resolution. Works with data owners, governance personnel, and customer-facing teams to ensure data products are understandable, reliable, and ready for approved use.

Requirements

• U.S. Citizenship is required due to federal contract requirements.
• Candidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S.
• Candidate must have lived in the U.S. for three (3) full years out of the last five (5) years.
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Data Science, Statistics, Software Engineering, or related field; or a high school diploma with four (4) additional years of relevant experience in lieu of a bachelor's degree.
• Minimum 3 years of relevant experience aligned to the responsibilities of this role.
• Master's degree may substitute for two (2) years of relevant experience.

Nice To Haves

• Experience designing, building, and maintaining modern cloud-based data pipelines, data products, and analytical data platforms.
• Strong experience with Databricks, Delta Lake, Apache Spark, SQL, and Python for large-scale data processing and transformation workloads.
• Experience implementing ETL/ELT pipelines utilizing Databricks Workflows, Azure Data Factory, Spark, notebooks, orchestration frameworks, and modern DataOps practices.
• Experience designing and implementing Lakehouse data architectures, including bronze, silver, and gold layers, optimized data storage patterns, and scalable analytical data structures.
• Experience developing semantic views, conformed dimensions, curated data products, and consumption-ready datasets that support analytics, reporting, self-service BI, and AI/ML workloads.
• Experience supporting enterprise data warehouse modernization, migration, and cloud transformation initiatives involving legacy reporting and analytics environments.
• Experience implementing and maintaining data quality controls, validation checks, exception handling, automated testing, and release-readiness processes within production data pipelines.
• Experience developing and maintaining metadata, lineage documentation, pipeline documentation, operational runbooks, and technical design artifacts.
• Experience utilizing Unity Catalog, metadata management solutions, and governance controls to support discoverable, trusted, and governed data assets.
• Experience supporting Power BI, enterprise reporting platforms, semantic models, and analytical consumption layers.
• Experience designing data models that support machine learning, predictive analytics, data science, NLP, and generative AI use cases.
• Experience supporting MLOps and AI/ML workflows, including feature engineering, training-data preparation, model serving support, and data product lifecycle management.
• Experience implementing DataOps practices including automated deployments, CI/CD integration, observability, monitoring, defect resolution, and operational telemetry.
• Experience collaborating with Data Owners, Product Owners, Governance teams, Data Scientists, AI Engineers, Architects, and customer stakeholders to deliver production-ready data products.
• Familiarity with cloud-native data services including Azure Data Factory, Azure Machine Learning, Azure storage services, Azure Event Hubs, and comparable technologies.
• Experience supporting Federal government, healthcare, or other highly regulated environments preferred.
• Experience working in Agile, DataOps, DevSecOps, or cross-functional delivery teams.
• Highly effective analytical, problem-solving, and decision-making capabilities.
• Excellent written and verbal communication skills, with the ability to work effectively across technical and non-technical audiences.
• Strong organization, attention to detail, and the ability to prioritize and manage multiple responsibilities.
• Collaborative approach with a commitment to quality, accountability, and continuous improvement.

Responsibilities

• Builds and maintains data pipelines, transformations, semantic views, conformed dimensions, and consumption-ready data models for analytics, reporting, and AI/ML use cases.
• Implements pipeline documentation, lineage support, release-readiness evidence, quality checks, and defect resolution.
• Works with data owners, governance personnel, and customer-facing teams to ensure data products are understandable, reliable, and ready for approved use.
• Apply ETL/ELT, SQL/Python, Databricks, Azure Data Factory or equivalent orchestration, semantic views, data modeling, quality checks, and documentation to support role delivery.
• Collaborate with relevant product, engineering, security, governance, quality, and customer-facing stakeholders as required by the role.
• Document work products, decisions, risks, and delivery evidence to support traceability and continuous improvement.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level