Senior Data Engineer

eSimplicity•Columbia, MD

2d•$132,400 - $151,800•Remote

About The Position

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow. This position is contingent upon contract award.

Requirements

All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation
10+ years of hands-on software development experience; 4+ years of data pipeline experience using Python, Java, and cloud technologies
Bachelor’s degree in Computer Science, Information Systems, Engineering, Business, or other related scientific or technical discipline
Strong understanding of AWS data services including S3, Redshift, RDS, EMR, AWS Glue, AWS Glue DataBrew, Athena, QuickSight, and Amazon SNS
Experience with Spark and Hive for big-data processing
Experience building job workflows on Databricks or comparable platforms
Familiarity with workflow orchestration tools such as Airflow, Luigi, or Azkaban
Experienced in data governance processes to ingest (batch and stream), curate, and share data with upstream and downstream consumers
Strong skills with relational, NoSQL, and SQL databases including Postgres and Cassandra
Familiarity with DevOps methodologies, including CI/CD pipelines (GitHub Actions) and IaC (Terraform)
Experience with Agile methodology and test-driven development

Nice To Haves

Federal Government contracting work experience, particularly with CMS, MACBIS, or other HHS programs
Experience working with Medicaid, MMIS, T-MSIS, APD/MDBT, or other CMS data sets
Experience integrating data with Salesforce via REST/SOAP APIs, Bulk API, and Salesforce Connect
Familiarity with Power BI semantic models, datamarts, and Microsoft Fabric / DataConnect equivalents
Experience supporting AI/ML feature engineering and feature stores
AWS Data Analytics or AWS Solutions Architect certification

Responsibilities

Designing, developing, and maintaining scalable data pipelines that ingest, reconcile, and validate Medicaid program data from MESH and connected CMS systems including T-MSIS, MBES, MacFin, SPoTT, and CMCS DataConnect
Building and operating data ingestion workflows that support mixed-modality content — spreadsheets (Excel, CSV), documents (PDF, Word), API feeds, and enterprise data services — with full lineage and audit traceability
Implementing the data layer that powers the Master Detailed Budget Table (MDBT) modernization, including parsing legacy and modern MDBT formats, normalizing line items (e.g., MMIS 2A/B, 4A/B, 5A/B/C), and validating total computable, state share, and federal share calculations
Developing API services to facilitate bidirectional data integration between MESH (Salesforce) and external systems, including standardized integrations with T-MSIS for compliance and Data Quality Assessment status
Building data processing workflows on AWS (S3, Glue, EMR, Athena, RDS/Redshift) and, where applicable, Databricks, Spark, and Hive for big-data processing
Creating data products that feed Power BI dashboards and Salesforce reports, ensuring consistent definitions, refresh schedules, snapshot management, and data-quality indicators
Implementing automated data quality, validation, and reconciliation jobs that flag funding decreases, mismatched FFP rates, missing submissions, and other anomalies
Supporting AI/ML pipelines for the MESH platform — including features for AI-assisted submission analysis, predictive analytics, and anomaly detection — by providing curated, governed training and inference data
Writing unit and integration tests for all data processing code and partnering with DevOps engineers on CI, CD, and IaC (GitHub Actions, Terraform) so pipelines deploy reliably
Performing code reviews, contributing to data-engineering standards, and mentoring less experienced engineers on pipeline design, performance optimization, and observability
Maintaining data security and privacy controls for data at rest and in transit (FIPS 140 validated encryption, KMS-managed keys, least-privilege access) consistent with CMS ARS 5.1 and FedRAMP Moderate baselines
Documenting data dictionaries, lineage, calculation logic, and pipeline runbooks in CMS-approved tools (Confluence, GitHub) to support transparency, audit, and contract transition activities