Health Data Engineer - Mid

Location: Tysons Corner

Overview

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.

Responsibilities

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.

* Data Pipeline Development:

* 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.

* Database Management & Optimization:

* Develop and maintain robust, scalable data storage solutions such as relational databases (SQL Server, Postgre

SQL, MySQL) or No

SQL databases (Mongo

DB, Cassandra, Dynamo

DB).

* 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.

* Healthcare Data Integration & Analytics Support:

* 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.

* Security, Compliance & Monitoring:

* 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.

* Collaboration and Documentation:

* Work closely with developers, Dev Ops 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.

Qualifications

MINIMUM QUALIFICATIONS

* 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, Postgre

SQL, or No

SQL databases like Mongo

DB).

* Strong knowledge of ETL/ELT processes, data warehousing, and analytics frameworks (e.g., Snowflake, Redshift, Big Query, 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…