Role: Data Engineer

Client: MD State

Reference# 71

Number of Positions: Multiple

Location: Primary place of performance is HYBRID with 40% onsite requirement in Baltimore City, MD. One week per month onsite is required. On-site attendance may also be required during system rollout activities in addition to the monthly requirement. May require travel to other offices in the Baltimore area as needed.

About the Role: The client 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 multiple projects as needed.

Position Description: Responsible for designing, building, and maintaining data pipelines and infrastructure to support data-driven decisions and analytics. The individual is responsible for the following tasks:

A. Design, develop, and maintain data pipelines, and extract, transform, load (ETL) processes to collect, process, and store structured and unstructured data

B. Build data architecture and storage solutions, including data lakehouses, data lakes, data warehouse, and data marts to support analytics and reporting

C. Develop data reliability, efficiency, and quality checks and processes

D. Prepare data for data modeling

E. Monitor and optimize data architecture and data processing systems

F. Collaboration with multiple teams to understand requirements and objectives

G. Administer testing and troubleshooting related to performance, reliability, and scalability

H. Create and update documentation

Role and Responsibilities:

Hands-On Data Pipeline Development

• 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.
• EDW & Data Lake Implementation
• 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.
• Healthcare Data Integration
• 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.
• Data Quality, Security & Compliance
• Develop automated validation scripts to enforce data quality and integrity.
• Implement IAM roles, encryption, and auditing to meet HIP nd CMS compliance standards.
• Maintain lineage and governance documentation for all pipelines.
• Collaboration & Delivery
• 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.

Education: This position requires a bachelor's or master's degree from an accredited college or university with a major in computer science, statistics, mathematics, economics, or a related field. Three (3) years of equivalent experience in a related field may be substituted for the Bachelor's degree.

General Experience: The proposed candidate must have a minimum of three (3) years of experience as a data engineer.

Specialized experience: The candidate should have experience as a data engineer or in a 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 about distributed computing and parallel processing.

• 3+ years of 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 Jav 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 HIP nd CMS interoperability requirements.