Job description

Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award. The Work At ICF our 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. Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC. *If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections. What You Will Do: 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. Basic Qualifications 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. Preferred Qualifications 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. Professional Skills: 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. #LI-CC1 #Indeed Bot and Third-Party Applications Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration . Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact Candidateaccommodation icf.com for assistance. Working at ICF ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world s toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer . Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our  EEO policy. We will consider for employment qualified applicants with arrest and conviction records. Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email  Candidateaccommodation icf.com  and we will be happy to assist . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.  Read more about  workplace discrimination righ t s or our benefit offerings which are included in the  Transparency in (Benefits) Coverage Act. Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted . This policy is in place to maintain the integrity and authenticity of the interview process.  However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at  candidateaccommodation icf.com . We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.   Pay Range - There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position. The pay range for this position based on full-time employment is : $81,499.00 - $138,549.00 Nationwide Remote Office (US99)

Requirements

Must-haves

Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award.
Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC.
If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections.
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.
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.

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlsqlpythonazuremachine learningci/cdagile

Platform: workday
Effort: long

Benefits

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