Data Engineer

ICF

. Build and maintain data pipelines, transformations, semantic views, conformed dimensions, and consumption-ready data models for analytics, reporting, and AI/ML use cases .

Posted 10/8/2026full-timeUnited StatesMid-LevelSenior💰 $81,499 - $138,549 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining cloud-based data pipelines, implementing data quality controls, and developing semantic views for analytics and AI/ML use cases. Proficient in collaboration with cross-functional teams to ensure data products meet governance and quality standards.

Highest-signal resume keywords

Data Pipeline DevelopmentDatabricks and Delta LakeETL/ELT and SQLData Quality ControlsCloud Data Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonDatabricksApache SparkData ModelingDataOps PracticesMLOpsAutomated TestingMetadata ManagementAzure Data Factory

Soft Skills

Analytical SkillsProblem-SolvingDecision-MakingCommunication SkillsOrganization

Tools & Technologies

Azure Machine LearningPower BIUnity CatalogAzure Storage ServicesAzure Event Hubs

Industry Keywords

Federal GovernmentHealthcareData GovernanceAgileDevSecOps

Tech Stack

Tools & technologies

ApacheAzureCloudETLPythonSparkSQLUnity

About the role

Key responsibilities & impact

Build and maintain data pipelines, transformations, semantic views, conformed dimensions, and consumption-ready data models for analytics, reporting, and AI/ML use cases
Implement pipeline documentation, lineage support, release-readiness evidence, quality checks, and defect resolution
Work 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
Collaborate with product, engineering, security, governance, quality, and customer-facing stakeholders
Document work products, decisions, risks, and delivery evidence to support traceability and continuous improvement

Requirements

What you’ll need

U.S. Citizenship required due to federal contract requirements
Must reside in the U.S., be authorized to work in the U.S., and perform all work in the U.S.
Must have lived in the U.S. for three full years out of the last five 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 additional years of relevant experience in lieu of a bachelor's degree
Minimum 3 years of relevant experience aligned to the role responsibilities
Master's degree may substitute for two years of relevant experience
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
Experience with Databricks Workflows, Azure Data Factory, Spark, notebooks, orchestration frameworks, and DataOps practices
Experience designing and implementing Lakehouse architectures, including bronze, silver, and gold layers
Experience developing semantic views, conformed dimensions, curated data products, and consumption-ready datasets
Experience supporting data warehouse modernization, migration, and cloud transformation initiatives
Experience implementing data quality controls, validation checks, exception handling, automated testing, and release-readiness processes
Experience developing metadata, lineage documentation, pipeline documentation, operational runbooks, and technical design artifacts
Experience with Unity Catalog, metadata management solutions, and governance controls
Experience supporting Power BI, enterprise reporting platforms, semantic models, and analytical consumption layers
Experience designing data models for machine learning, predictive analytics, data science, NLP, and generative AI use cases
Experience supporting MLOps and AI/ML workflows
Experience implementing DataOps practices, 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
Familiarity with 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
Strong organization, attention to detail, prioritization, and multitasking abilities

Benefits

Comp & perks

Remote work arrangement
Occasional onsite meetings on the client site in Washington, DC
Equal opportunity employer
Reasonable accommodations for disabilities, veterans, and sincerely held religious beliefs
Benefits information provided under the Transparency in Coverage Act