• Support the development and maintenance of secure, scalable data systems that enable clinical, operational, and financial analytics in a HIPAA‑regulated environment. • Contribute to building and operating data pipelines that ingest, transform, and standardize data from internal and external healthcare sources such as EHRs, claims vendors, and SDOH APIs. • Work closely with analysts and business stakeholders to understand requirements and help create reliable, analytics‑ready datasets and basic data models. • Assist in documenting data processes and governance practices, including data lineage, access controls, and handling procedures for sensitive health information. • Participate in data quality efforts by implementing validation checks, monitoring data flows, and helping identify anomalies that could impact downstream analytics. • Help develop and maintain reusable dbt models and shared data transformations, with exposure to open healthcare data models like Tuva, OMOP, or FHIR. • Collaborate with senior engineers to improve infrastructure and team processes, taking on increasing responsibility as skills grow and the data engineering function expands.

• Build and maintain automated data workflows and orchestrations using Apache Airflow • Build automation processes using Copilot to generate Airflow DAGs • Implement at least two major end-to-end data pipeline projects using Airflow • Design and optimize complex DAGs for scalability, maintainability, and reliability • Create reusable, parameterized, and modular Airflow components (operators, sensors, hooks) to streamline workflow development • Ensure effective monitoring, alerting, and logging of Airflow DAGs for quick issue resolution • Document workflows, solutions, and processes for team knowledge sharing and training • Mentor and support other team members in Airflow usage and adoption • Explain best practices, identify pros and cons, and communicate technical decisions to team members • Develop reusable frameworks, leveraging reusable concepts for efficiency and scalability • Implement and utilize reusable ecosystem components, including Python & Apache Airflow, DynamoDB, Amazon RDS • Develop reusable frameworks to enforce data governance and data quality standards • CI/CD pipeline development using re-usable frameworks and Jenkins

• DecisionPoint seeks a Data Architect to lead the design, governance, and validation of enterprise data architectures for a Department of Defense (DoD) modernization program supporting Casualty and Mortuary Affairs (CMA) operations across all Service components. • The Data Architect will define and maintain logical and physical data models across Impact Level (IL) 2, 4, and 5 environments, ensuring high availability, data integrity, and secure data exchange within a cloud-hosted ecosystem. • The Data Architect will work closely with system engineers, database administrators, and developers to standardize schemas, improve data quality, and align data governance with DoD enterprise architecture standards.

• Provide advanced analytical, machine learning, and data engineering support. • Design, develop, and deploy machine learning models for classification, regression, time series forecasting, and natural language processing applications. • Build and optimize automated, scalable ETL/ELT pipelines using Python, SQL, and cloud-based tools. • Develop and maintain production ML systems including model deployment, monitoring, versioning, and performance tracking. • Design, develop, and deploy interactive dashboards and data visualizations. • Perform end-to-end model development including exploratory data analysis, feature engineering, hyperparameter tuning, model validation, and documentation. • Develop and maintain data pipelines and workflows using tools such as AWS services, Databricks, and GitLab CI/CD. • Conduct data mining, cleaning, and manipulation using SQL, Python (Pandas, NumPy), or R.