Guidehouse is seeking a Data Engineer Lead to lead the design, development, and modernization of enterprise data platforms, analytics ecosystems, and cloud-native data solutions. This role serves as the technical leader for data engineering initiatives, providing architecture guidance, mentoring engineering teams, and driving the implementation of scalable, secure, and high-performing data solutions that support mission-critical business and analytical needs.

• Lead the design, development, and deployment of modern data platforms, pipelines, and analytics solutions across cloud and hybrid environments.

• Define technical architecture, engineering standards, and best practices for data integration, storage, processing, and governance.

• Architect and oversee the implementation of scalable ETL/ELT workflows, data lakehouses, and enterprise data platforms.

• Lead data modernization and migration initiatives, including cloud adoption, platform transformation, and legacy system replacement efforts.

• Provide technical leadership and mentorship to data engineers, fostering engineering excellence and professional development.

• Collaborate with solution architects, data scientists, business analysts, and client stakeholders to define technical strategies and implementation roadmaps.

• Design and optimize data models, data warehouses, and large-scale analytical environments to support reporting, operational analytics, and advanced analytics use cases.

• Establish and enforce data quality, observability, security, reliability, and operational support processes.

• Lead the adoption of DevSecOps and CI/CD practices to automate deployments and improve platform reliability.

• Oversee performance tuning, troubleshooting, and optimization of data platforms and processing workloads.

• Conduct technical reviews, architecture assessments, and code reviews to ensure delivery quality and adherence to best practices.

• Support proposal development, solutioning, technical estimations, and business development activities.

• Serve as a trusted technical advisor to project leadership and client stakeholders.

• Role is contingent upon contract award

What You Will Need:

• 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.

• SEVEN (7) or more years of experience in data engineering, software engineering, analytics engineering, or related technical disciplines.

• Experience leading technical teams and complex data engineering initiatives.

• Experience with Python, SQL, Spark/PySpark, and modern data engineering frameworks.

• Experience designing, developing, and optimizing large-scale data pipelines and ETL/ELT solutions.

• Experience architecting enterprise data platforms, data lakes, data warehouses, and cloud-native data solutions.

• Significant hands-on experience with AWS, Azure, or multi-cloud environments.

• Experience with Databricks, Snowflake, or similar distributed data processing platforms.

• Experience implementing and managing CI/CD pipelines, infrastructure automation, and DevOps practices.

• Experience with containerization and orchestration technologies, including Docker and Kubernetes.

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

• Strong understanding of data governance, metadata management, data quality, and data security principles.

• Experience leading Agile development teams and collaborating across cross-functional technical organizations.

• Strong analytical, problem-solving, and architectural decision-making skills.

• Demonstrated ability to communicate technical concepts to both technical and non-technical audiences.

• Experience developing technical documentation, architecture artifacts, and engineering standards.

• Experience designing and implementing Databricks Lakehouse, Delta Lake, Fabric, Snowflake, or similar modern analytics architectures.

• Experience with event-driven and real-time data architectures using Kafka, Event Hub, Kinesis, or similar technologies.

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

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

• Experience supporting AI/ML platforms and operationalizing machine learning workflows.

• AWS, Azure, Databricks, Snowflake, or Kubernetes certifications.

• Experience leading large-scale data modernization or digital transformation programs.

• Previous consulting experience with responsibility for client-facing technical leadership.

• Experience contributing to business development activities, technical proposals, and solution architecture.