Developer/Data Engineer

CMT Services Inc•Baltimore, MD

2d•Hybrid

About The Position

Maryland Department of Health is seeking a hands-on Data Engineer to design, develop, and optimize large-scale data pipelines in support of our Enterprise Data Warehouse (EDW) and Data Lake solutions. This role requires deep technical expertise in coding, pipeline orchestration, and cloud-native data engineering on AWS. The Data Engineer will be directly responsible for implementing ingestion, transformation, and integration workflows — ensuring data is high-quality, compliant, and analytics-ready. This role may support other projects or teams within MDH as needed.

Requirements

The proposed candidate must have a minimum of three (3) years of experience as a data engineer.
A bachelor's or master's degree from an accredited college or university with a major in computer science, statistics, mathematics, economics, or related field. Three (3) years of equivalent experience in a related field may be substituted for the bachelor's degree.
The candidate should have experience as data engineer or similar role with a strong understanding of data architecture and ETL processes.
The candidate should be proficient in programming languages for data processing and knowledgeable of distributed computing and parallel processing.
3+ years hands-on experience in building, deploying, and maintaining data pipelines on AWS or equivalent cloud platforms.
Strong coding skills in Python and SQL (Scala or Java a plus).
Proven experience with Apache Spark (PySpark) for large-scale processing.
Hands-on experience with AWS Glue, S3, Redshift, Athena, EMR, Lake Formation.
Strong debugging and performance optimization skills in distributed systems.
Hands-on experience with Iceberg, Delta Lake, or other OTF table formats.
Experience with Airflow or other pipeline orchestration frameworks.
Practical experience in CI/CD and Infrastructure-as-Code (Terraform, CloudFormation).
Practical experience with EDI X12, HL7, or FHIR data formats.
Strong understanding of Medallion Architecture for data lake houses.
Hands-on experience building dimensional models and data warehouses.
Working knowledge of HIPAA and CMS interoperability requirements.

Responsibilities

Design, develop and maintain data pipelines, and extract, transform, load (ETL) processes to collect, process and store structured and unstructured data
Build data architecture and storage solutions, including data lakehouses, data lakes, data warehouse, and data marts to support analytics and reporting
Develop data reliability, efficiency, and qualify checks and processes
Prepare data for data modeling
Monitor and optimize data architecture and data processing systems
Collaboration with multiple teams to understand requirements and objectives
Administer testing and troubleshooting related to performance, reliability, and scalability
Create and update documentation
Design, code, and deploy ETL/ELT pipelines across bronze, silver, and gold layers of the Data Lakehouse.
Build ingestion pipelines for structured (SQL), semi-structured (JSON, XML), and unstructured data using PySpark/Python programming language using AWS Glue or EMR.
Implement incremental loads, deduplication, error handling, and data validation.
Actively troubleshoot, debug, and optimize pipelines for scalability and cost efficiency.
Develop dimensional data models (Star Schema, Snowflake Schema) for analytics and reporting.
Build and maintain tables in Iceberg, Delta Lake, or equivalent OTF formats.
Optimize partitioning, indexing, and metadata for fast query performance.
Build ingestion and transformation pipelines for EDI X12 transactions (837, 835, 278, etc.).
Implement mapping and transformation of EDI data with FHIR and HL7 frameworks.
Work hands-on with AWS Health Lake (or equivalent) to store and query healthcare data.
Develop automated validation scripts to enforce data quality and integrity.
Implement IAM roles, encryption, and auditing to meet HIPAA and CMS compliance standards.
Maintain lineage and governance documentation for all pipelines.
Work closely with the Lead Data Engineer, analysts, and data scientists to deliver pipelines that support enterprise-wide analytics.
Actively contribute to CI/CD pipelines, Infrastructure-as-Code (IaC), and automation.
Continuously improve pipelines and adopt new technologies where appropriate.