Senior ETL & Data Pipeline Engineer

Place of Performance: HYBRID with 40% onsite requirement in Baltimore City or Linthicum MD Performance Period: 12-months with 1 one-year renewal option.

Anticipated

Start Date:

July 1, 2026

Need Local to Maryland Seeking a seasoned, highly skilled ETL developer whose responsibilities include designing, building, automating, and maintaining sophisticated programs that extract, convert, and load data into the Provider Management Module (PMM). This role may also support other projects within the Project Management Office(PMO) 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 lake houses, data lakes, data warehouse, and data marts to support analytics and reporting

C. Develop data reliability, efficiency, and qualify 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:

Design and implement robust, scalable data models to support the application, analytics and business intelligence initiatives.

Optimize data warehousing solutions and manage data migrations in the AWS ecosystem, utilizing Amazon Redshift, RDS, and Document

DB services.

Develop and maintain scalable ETL pipelines using AWS Glue and other AWS services to enhance data collection, integration, and aggregation.

Ensure data integrity and timeliness in the data pipeline, troubleshooting any issues that arise during data processing.

Integrate data from various sources using AWS technologies, ensuring seamless data flow across systems.

Collaborate with stakeholders to define data ingestion requirements and implement solutions to meet business needs.

Monitor, tune, and manage database performance to ensure efficient data loads and queries.

Implement best practices for data management within AWS to optimize storage and computing costs.

Ensure all data practices comply with regulatory requirements and department policies.

Implement and maintain security measures to protect data within AWS services.

Lead and mentor junior data engineers and team members on AWS best practices and technical challenges.

Collaborate with UI/API team, business analysts, and other stakeholders to support data-driven decision-making.

Explore and adopt new technologies within the AWS cloud to enhance the capabilities of the data platform.

Continuously improve existing systems by analyzing business needs and technology trends.

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 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 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.

Minimum 5 + years ETL coding experience

Proficiency in programming languages such as Python and SQL for data processing and automation

Experience with distributed computing frameworks like Apache Spark or similar technologies

Experience with AWS data environment, primarily Glue, S3, Document

DB, Redshift, RDS, Athena, etc.

Experience with data warehouses/RDBMS like Redshift and No

SQL data stores such as Document

DB, Dynamo

DB, Open Search, etc Experience in building data lakes using AWS Lake Formation

Experience with workflow orchestration and scheduling tools like AWS Step Functions, AWS MWAA, etc..Strong understanding of relational databases (including tables, views, indexes, table spaces)

Experience with source control tools such as Git Hub and related CI/CD processes

Ability to analyze a company’s data needs

Strong problem-solving skills

Experience with the SDLC and Agile methodologies
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