Data Engineering & Reconciliation Specialist

Diligent Solutions•,

5d•Remote

About The Position

The ideal candidate possesses experience analyzing end-to-end data flows, identifying reconciliation breakpoints, validating control execution, and translating business and audit requirements into actionable data engineering and reconciliation solutions. This individual will help strengthen data quality, improve control traceability, and support remediation activities across multiple enterprise systems.

Requirements

Active Secret Clearance or Interim Secret Clearance with the ability to obtain and maintain a Secret Clearance.
Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, Business, or a related discipline. Equivalent professional experience may be considered in lieu of a degree.
Minimum of 5 years of relevant professional experience supporting data engineering, data analysis, data quality, reconciliation, business intelligence, enterprise reporting, or related technical initiatives. Candidates with 2+ years of highly relevant experience are also encouraged to apply.
Strong ability to analyze technical data flows and business processes from end to end.
Experience identifying data quality issues, reconciliation breakpoints, control gaps, and traceability challenges.
Experience translating business and control requirements into technical solutions.
Experience creating and maintaining source-to-target mappings, data lineage documentation, and reconciliation logic.
Strong analytical and problem-solving skills with the ability to isolate issues across systems, interfaces, and data structures.
Experience developing validation rules, reconciliation logic, and exception-handling processes.
Strong documentation skills with exceptional attention to traceability, evidence quality, and accuracy.
Experience collaborating across business, technology, audit, governance, and data management teams.
Familiarity with data quality concepts, data profiling, and structured data for reporting and analytics.
Excellent written and verbal communication skills.

Nice To Haves

Experience supporting data reconciliation, control traceability, audit readiness, or IT audit remediation initiatives.
Experience working with enterprise systems, interfaces, or complex data integration environments.
Experience creating and maintaining databases or developing data analytics solutions.
Experience with one or more of the following: Python, SQL, R, Databricks, Scala, & Visual Basic.
Experience supporting Power BI or similar enterprise analytics platforms.
Experience working within Department of Defense (DoD) or Department of the Air Force (DAF) environments.
Experience supporting enterprise data governance, audit remediation, or financial systems modernization initiatives.
Experience developing: Data visualizations, Data analytics reports, Data cleansing scripts and processes, Technical data analytics

Responsibilities

Aggregate and review Process Control Matrix (PCM) content for assigned business processes and systems.
Review third-party system dependencies, including SOC reports and Complementary User Entity Controls (CUECs), and support incorporation of applicable CUECs into Governance, Risk, and Compliance (GRC) processes.
Support walkthroughs with system owners to trace end-to-end data flows, validate control execution, and confirm evidence requirements.
Conduct deep-dive analyses to identify reconciliation breakpoints, root causes, and downstream impacts.
Translate business and audit requirements into data rules, mappings, reconciliation logic, and validation requirements.
Design and maintain source-to-target mappings, reconciliation logic, and exception-handling rules.
Support testing activities to validate the effectiveness of implemented solutions.
Prepare evidence packages, traceability matrices, issue logs, and supporting documentation for audit and remediation activities.
Develop reporting inputs and structured, AI-ready datasets to support Power BI dashboards and analytics.
Collaborate with system owners, interface specialists, IT audit teams, data quality leads, and business stakeholders to ensure consistency and traceability across project deliverables.
Maintain documentation of assumptions, dependencies, limitations, risks, and open issues.
Support remediation tracking, solution validation, re-testing, and post-implementation verification.