Health Data Engineer - Mid

Logistics Management Institute

About The Position

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. We are seeking a results-driven Mid-Level Data Engineer to join our team and contribute to developing cutting-edge healthcare data solutions. This role involves building, managing, and optimizing data pipelines, ensuring the reliable acquisition, storage, transformation, and delivery of data for our health-focused applications and analytics platforms with a primary focus on Medicare, Medicare Advantage, and risk adjustment. The ideal candidate will bring a strong background in data engineering and analytics, as well as familiarity with healthcare data and compliance requirements. You will play a critical role in ensuring data integrity, accessibility, and security, ultimately facilitating valuable insights and better outcomes for patients, healthcare providers, and stakeholders. This is an exceptional opportunity to use your technical and analytical skills to make an impact in the healthcare space, working with large datasets to drive innovative solutions that enhance patient care and healthcare processes.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
Minimum of 5 years of professional experience as a Data Engineer, Data Analyst, or a similar role, preferably with exposure to healthcare data projects or financial/banking systems.
Proficiency with structured and unstructured data querying tools (SQL, PostgreSQL, or NoSQL databases like MongoDB).
Strong knowledge of ETL/ELT processes, data warehousing, and analytics frameworks (e.g., Snowflake, Redshift, BigQuery, Databricks, or Apache Spark).
Hands-on experience with data pipeline tools such as Apache Airflow, Talend, NiFi, or similar platforms.
Familiarity with cloud platforms like AWS, Azure, or Google Cloud, including tools for data processing (e.g., AWS Glue, S3, Athena).
Expertise in programming/scripting languages like Python, PySpark, Java, or Scala for data manipulation and automation tasks.
Knowledge of containerization tools, including Docker and Kubernetes, for data system deployment.
Familiarity with data visualization tools (e.g., Tableau, Power BI) and an understanding of analytics concepts.
Understanding of healthcare data security and compliance requirements (e.g., HIPAA).
Experience with encryption, data masking, and access controls to protect sensitive health information.
Strong analytical and problem-solving capabilities, with the ability to work with large-scale datasets and maintain their integrity.
Excellent communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
Ability to work in a fast-paced healthcare setting and adapt to evolving project requirements.
Strong attention to detail, organization, and commitment to quality.

Nice To Haves

Familiarity with healthcare compliance requirements such as HIPAA or GDPR.
Certifications such as AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, or Linux Foundation Certified Kubernetes Administrator.
Experience in federal consulting.
Demonstrated experience with health projects, including those involving Medicare and health data.

Responsibilities

Architect and build scalable, reliable, and secure data pipelines to gather, process, and store healthcare data from multiple sources.
Create and optimize ETL/ELT workflows, ensuring proper data extraction, transformation, and loading into analytics-ready databases or data warehouses.
Design, implement, and manage cloud-based data engineering solutions using cloud platforms.
Develop and maintain robust, scalable data storage solutions such as relational databases (SQL Server, PostgreSQL, MySQL) or NoSQL databases (MongoDB, Cassandra, DynamoDB).
Tune database performance, troubleshoot issues, and implement optimizations to handle large-scale data sets efficiently.
Ensure data reliability and integrity through schema design, normalization, and validation processes.
Integrate data across healthcare applications and systems
Prepare clean, well-organized datasets for downstream analytics and machine learning projects to improve patient outcomes and healthcare workflows.
Collaborate with data analysts and product teams to provide data solutions that support reporting, dashboards, and decision-making tools.
Implement security measures to protect sensitive healthcare data and ensure compliance with healthcare regulations (e.g., HIPAA).
Monitor data pipelines and infrastructure, addressing bottlenecks or failures to ensure system uptime and data accessibility.
Proactively identify and address security vulnerabilities or inconsistencies in data systems.
Work closely with developers, DevOps engineers, product managers, and healthcare specialists to understand requirements, share insights, and align on project goals.
Document data models, pipelines, API integrations, and best practices for reference and knowledge sharing.
Contribute to the overall technical strategy for optimizing health data processing and usage.