Role Description DecisionPoint seeks a Data Engineer to design, develop, and maintain secure data pipelines, transformations, and analytics infrastructure supporting enterprise systems within a Department of Defense (DoD) modernization initiative. The Data Engineer will ensure accurate, reliable, and performant data flow across applications, integrating structured and unstructured data from multiple mission sources into cloud-based storage and analytics environments. This position plays a critical role in enabling interoperability, analytics, and automation across AWS GovCloud IL2–IL5 environments through the use of scalable data engineering practices and secure DevSecOps integration. This position is fully remote. The Data Engineer will: - Design, implement, and maintain data pipelines and ETL processes supporting ingestion, transformation, and validation of mission data. - Develop and optimize data models and schemas across relational and non-relational databases to support system integrations and analytics. - Collaborate with system architects, integration developers, and data analysts to ensure data consistency, security, and integrity across cloud environments. - Implement data migration and synchronization between legacy systems, applications, and modern cloud platforms. - Utilize AWS services (Glue, Lambda, S3, RDS, Redshift, Kinesis) to build and sustain scalable and fault-tolerant data infrastructure. - Support data validation and reconciliation, performing quality checks and developing reports to ensure accuracy. - Integrate data from APIs, streaming sources, and file-based systems into centralized repositories or data lakes. - Automate data workflows using infrastructure-as-code and CI/CD principles to ensure repeatability and efficiency. - Monitor and troubleshoot data pipeline performance, ensuring adherence to SLAs and operational reliability. - Implement data encryption, masking, and access controls in compliance with DoD cybersecurity policies and RMF requirements. - Support development of dashboards and analytics products, enabling data-driven insights for mission stakeholders. - Maintain documentation and metadata repositories, including data dictionaries, lineage, and technical specifications. - Participate in Agile sprints, contributing to backlog refinement, testing, and cross-functional collaboration. Qualifications - Clearance Requirement: Must hold an active Secret clearance. - Education: Bachelor’s degree in Computer Science, Information Systems, or a related technical field. - Experience: Minimum 7 years of experience in data engineering, integration, or analytics enablement. - Experience building and optimizing data pipelines in enterprise or federal environments. - Familiarity with Agile software development and DevSecOps delivery frameworks. - Related technical certification required. Requirements - Proficiency with Python, SQL, and ETL frameworks (e.g., Apache NiFi, Talend, or AWS Glue). - Experience with cloud data services, preferably AWS GovCloud (RDS, S3, Lambda, Glue, Redshift). - Knowledge of database management systems (PostgreSQL, SQL Server, DynamoDB, MongoDB). - Familiarity with RESTful API integration and data exchange standards (JSON, XML, CSV). - Understanding of DoD data security and compliance standards, including encryption, RMF, and STIG adherence. - Experience developing and maintaining data validation, profiling, and transformation scripts. - Exposure to data visualization and analytics tools (Power BI, Tableau, QuickSight). Certifications (Preferred) - AWS Certified Data Engineer – Associate or AWS Certified Developer. - CompTIA Security+ CE or equivalent DoD 8570 certification. - Certified Data Management Professional (CDMP) or equivalent. Skills - Strong analytical and problem-solving skills for data integration and performance tuning. - Excellent communication and collaboration across technical and functional teams. - Attention to detail with focus on data accuracy and consistency. - Ability to balance data governance, security, and performance objectives. - Commitment to continuous improvement and mission impact through data-driven decision-making.

Role Description - Diseñar, desarrollar y mantener soluciones de ingeniería de datos sobre ecosistema Microsoft Azure. - Diseñar y construir modelos de datos optimizados para entornos analíticos y grandes volúmenes de información. - Desarrollar pipelines de integración y transformación de datos (ETL/ELT) utilizando Azure Databricks y Azure Data Factory. - Crear y optimizar consultas SQL, procedimientos almacenados y estructuras de bases de datos. - Participar en el diseño de arquitecturas de datos escalables y eficientes. - Implementar procesos de carga, transformación y explotación de datos para diferentes áreas de negocio. - Garantizar la calidad, integridad y disponibilidad de la información. - Colaborar con equipos multidisciplinares en entornos Agile. - Participar en iniciativas de mejora continua y automatización de procesos de datos. - Documentar desarrollos, procesos y soluciones implementadas. Qualifications - Experiencia mínima de 4 años como Data Engineer. - Experiencia demostrable en Azure Databricks, Azure Synapse Analytics, Azure Data Factory y Azure SQL Database. - Experiencia sólida en diseño y construcción de modelos de datos. - Experiencia en definición de tablas, relaciones complejas, optimización de consultas y procedimientos almacenados. - Experiencia en desarrollo de pipelines y transformaciones de datos complejas en Databricks. - Nivel avanzado de SQL. - Experiencia con Python para procesamiento y transformación de datos. - Experiencia trabajando en proyectos bajo metodologías Agile. - Nivel de inglés mínimo B2. - Capacidad para trabajar de forma autónoma en entornos complejos de ingeniería de datos.

• Architect how GitLab and third-party data is ingested, modeled, and synchronized into the Knowledge Graph as a near-real-time graph of the development ecosystem. • Coordinate integration with external systems such as Jira, observability tools, Zendesk, and ServiceNow to add business context to data products. • Design the Data Marketplace so customers can consume GitLab data through Snowflake, Databricks, and BigQuery without engineering handoffs. • Publish GitLab observability data as OpenTelemetry and support the APIs used for data access. • Define operational readiness for the systems your team ships across GitLab.com, Dedicated, and Self-Managed deployments. • Translate ambiguous product problems into practical technical plans and iterative roadmaps in partnership with Product, Design, and the graph backend team. • Resolve cross-team coordination with Graph Backend, AI Platform, and Infrastructure to keep delivery moving. • Mentor senior and intermediate engineers through design reviews, pairing, and code review.

• Define and maintain data governance standards including access controls, data lineage, and contracts between data producers and consumers • Lead the technical direction and evolution of our data platform as we move toward an AI-first data infrastructure. You will design for AI consumption, unstructured data access, and integration with AI tooling from the ground up. • Build and scale data pipelines and architectures that make data accessible and useful to both human analysts and AI systems. • Own end-to-end design and development of scalable data pipelines, from ingestion and orchestration to transformation and delivery, using tools including AWS, Terraform, Airflow, Snowflake, dbt, Meltano, Python. • Drive data platform reliability through performance optimization, data quality monitoring, and SLA-based prioritization of our most critical data assets. • Leverage and champion AI-assisted development tools, including Claude Code, to accelerate development velocity across the team. • Mentor data engineers and analysts, raising the technical bar across the team.