Senior Data Engineer

Guidehouse seeks a Senior Data Engineer to design, develop, and optimize modern data platforms, pipelines, and cloud-based analytics solutions. The ideal candidate will have hands-on experience building scalable data ecosystems, strong software engineering fundamentals, and the ability to lead technical efforts while collaborating with multidisciplinary teams to deliver mission-critical data solutions.

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes supporting analytics, reporting, and operational workloads.

• Build and optimize data architectures, data models, and storage solutions across cloud and hybrid environments.

• Develop and maintain data engineering solutions using Python, SQL, Spark, and related technologies.

• Implement and support cloud-native data platforms leveraging AWS, Azure, Databricks, and other modern technologies.

• Design and implement CI/CD pipelines and DevOps best practices for data engineering workflows.

• Collaborate with architects, developers, analysts, and business stakeholders to translate requirements into technical solutions.

• Lead data integration, migration, and modernization initiatives, including legacy system transformation efforts.

• Ensure data quality, integrity, security, and governance standards are incorporated into solution designs.

• Monitor, troubleshoot, and optimize data pipelines and production environments to ensure reliability and performance.

• Mentor junior data engineers and support technical knowledge sharing across project teams.

• Contribute to technical architecture discussions, technology evaluations, and engineering best practices.

• Develop and maintain technical documentation including data flows, system designs, deployment procedures, and operational guides.

• Role contingent upon contract award.

• U.S. Citizenship or Green Card is required AND must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Data Science, or a related technical field.

• FOUR (4) or more years in data engineering, software engineering, analytics engineering, or a related discipline.

• Experience in Python, SQL, and data processing frameworks such as Spark or PySpark.

• Experience designing, developing, and supporting production-grade data pipelines and ETL/ELT processes.

• Experience working with relational and non-relational databases, including data modeling and query optimization.

• Experience with cloud platforms such as AWS and/or Azure.

• Experience with Databricks, Snowflake, or similar large-scale data processing environments.

• Experience implementing CI/CD pipelines and version control practices using tools such as Git, GitHub, GitLab, Jenkins, or Azure DevOps.

• Experience implementing data quality, monitoring, and operational support processes.

• Experience working within Agile software development environments.

• Experience supporting federal, healthcare, public health, or other highly regulated environments.

• Strong analytical, troubleshooting, and problem-solving skills.

• Ability to independently manage technical tasks and collaborate effectively across teams.

• Excellent verbal and written communication skills.

• Familiarity with containerization technologies such as Docker and orchestration platforms such as Kubernetes.

• Experience with AWS services such as S3, Redshift, Lambda, ECS, Glue, and SQS.

• Experience with Azure services such as Azure Data Factory, Synapse Analytics, Azure Functions, Cosmos DB, and Event Hub.

• Familiarity with modern data lakehouse architectures and data governance frameworks.

• Experience implementing Infrastructure-as-Code using Terraform, CloudFormation, or Bicep.

• Experience with monitoring and observability tools such as CloudWatch, Splunk, Kibana, Datadog, or Elasticsearch.

• Cloud, Databricks, Snowflake, or related technical certifications.

• Experience supporting large-scale data modernization or migration programs.

• Experience with machine learning data pipelines and AI-enabled solutions.

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