Data/ETL Engineer (Journeyman)

DecisionPoint | Cortek•Chicago, IL

2h•Remote

About The Position

DecisionPoint seeks a Journeyman Data/ETL Engineer to develop and maintain extract, transform, and load workflows supporting the USMEPCOM ENLIST program. The role will sustain SQL Server Integration Services (SSIS), build approved integrations, and implement raw, standardized, and curated data layers. The engineer will use SQL and Python to transform data, support API integrations, perform reconciliation checks, manage schema changes, maintain lineage, and troubleshoot pipeline issues. The Data/ETL Engineer will collaborate with senior engineers, database administrators, data governance personnel, BI developers, AI/ML teams, and testers to deliver accurate, traceable, and maintainable data for mission analysis and reporting. The position is remote, with Chicago-area candidates preferred. Applicants acknowledge that their resumes may be included in an active competitive government contract bid.

Requirements

Active Secret clearance or higher.
At least six years of relevant professional experience.
Bachelor’s degree in computer science, information systems, or a related field, or a permitted equivalent.
Experience developing and maintaining ETL workflows.
Experience supporting SQL Server Integration Services (SSIS).
Strong SQL skills, including experience developing SQL transformations.
Experience with Python and API integrations.
Experience developing and maintaining data mappings.
Experience building and executing reconciliation checks.
Experience maintaining pipeline documentation and data lineage.
Ability to support schema management and troubleshoot data pipelines.
Ability to implement raw, standardized, and curated data layers.
CompTIA Security+ CE or another approved IAT Level II certification when applicable to assigned duties.

Nice To Haves

Relevant data engineering certification.
Experience supporting federal or Department of Defense data environments.
Experience with data warehouses, analytical datasets, BI platforms, or AI-ready data.
Experience implementing incremental loads, checkpoints, retries, and failed-record handling.
Familiarity with metadata management, data governance, common identifiers, and authoritative sources.
Experience developing automated data-quality and regression checks.
Familiarity with version control, CI/CD, deployment automation, and rollback processes.
Familiarity with secure API authentication, service accounts, secrets management, and audit logging.
Strong troubleshooting, documentation, communication, and cross-team coordination skills.

Responsibilities

Develop, maintain, and troubleshoot ETL workflows using SSIS and approved integration tools.
Implement approved integrations between source systems, APIs, databases, and analytical or reporting environments.
Write and maintain SQL transformations, queries, and supporting database routines.
Use Python to automate data processing, validation, integration, and routine pipeline activities.
Implement raw, standardized, and curated data layers using approved designs and transformation rules.
Develop and maintain source-to-target, field, and identifier mappings.
Build reconciliation checks to verify record counts, totals, completeness, and transformation accuracy.
Support incremental loading, processing checkpoints, retries, and controlled reprocessing.
Implement approved schema changes and identify effects on dependent workflows and datasets.
Maintain data lineage, pipeline documentation, configuration records, and operating instructions.
Monitor scheduled processing, execution results, data freshness, and pipeline errors.
Investigate failures involving source data, SQL logic, SSIS packages, APIs, and downstream dependencies.
Document troubleshooting findings and escalate complex issues to senior engineers.
Correct assigned defects and support regression testing, reconciliation, and release validation.
Maintain version-controlled code and support approved deployment and rollback procedures.
Coordinate with governance, database, BI, AI/ML, and testing teams on data definitions, dependencies, and quality issues.
Update runbooks and technical documentation following pipeline changes and operational findings.