Data Governance Data Analyst

3Core Systems , Inc•Forks Township, PA

1d•Hybrid

About The Position

The Data Governance Data Analyst is a hands-on data analysis role supporting the quality, consistency, and understanding of enterprise data across multiple business systems. The analyst will use SQL and investigate complex datasets to understand how data moves through enterprise systems, data warehouses, and reporting environments. Working with business stakeholders, Data Engineering, and BI teams, the analyst will trace data lineage, investigate discrepancies, document data relationships, and improve data quality and transparency. The role emphasizes curious, independent data investigation rather than primarily developing dashboards, managing projects, or writing governance policies. The position is a six-month contract-to-hire role based in Easton, Pennsylvania, with a hybrid arrangement. During onboarding and the first few months, the expected onsite schedule is two days per week. After ramp-up, onsite attendance is approximately two days per month. Candidates must live in Easton or within reasonable commuting distance.

Requirements

Ideally 4–6 years of experience in data analysis, data quality, data management, or a related discipline.
Strong hands-on SQL skills, including writing queries, joining tables, filtering data, and investigating discrepancies.
Solid understanding of relational database concepts, including primary keys, foreign keys, table relationships, and data structures.
Experience with data mapping, data lineage, profiling, reconciliation, or data quality analysis.
Understanding of how data moves through ETL/ELT pipelines and enterprise data warehouses.
Experience working with complex enterprise datasets.
Ability to independently investigate data issues, identify patterns, and determine root causes.
Strong communication skills and experience collaborating with business users and technical teams.
Excellent analytical thinking, attention to detail, documentation skills, and problem-solving abilities.
Must be local to Easton, Pennsylvania, or within reasonable commuting distance.

Nice To Haves

Experience with Snowflake or similar cloud data warehouse platforms.
Experience with Power BI or Qlik Analytics.
Familiarity with Master Data Management (MDM) and enterprise data governance concepts.
Understanding of data modeling, dimensional modeling, data relationships, and data warehouse architecture.
Experience working with ERP, customer, product, inventory, supply chain, or manufacturing data.
Familiarity with metadata management, data catalogs, data stewardship, and data quality frameworks.
Basic Python experience for data analysis or automation.

Responsibilities

Write and execute SQL queries to analyze, profile, validate, and reconcile enterprise datasets.
Investigate data discrepancies, inconsistencies, and quality issues across multiple systems.
Use SQL joins, filters, and aggregations to understand relationships between datasets.
Identify root causes of data issues and collaborate with technical teams to support resolution.
Trace data from source systems through ETL/ELT pipelines, enterprise data warehouses, and reporting platforms.
Analyze and document source-to-target mappings, data transformations, and dependencies.
Understand how data is structured, modeled, transformed, and consumed across enterprise systems.
Identify gaps in data definitions, mappings, and documentation.
Support data governance initiatives focused on improving data accuracy, consistency, and reliability.
Help establish and document business definitions, data quality standards, and authoritative data sources.
Assist with data profiling, reconciliation, and validation activities.
Support enterprise data management initiatives, including master and reference data where applicable.
Partner with business users to understand how data is created, maintained, and used.
Clarify business processes, data definitions, and reporting requirements by asking thoughtful questions.
Collaborate with Data Engineering, BI, and other technical teams to investigate data-related issues.
Document findings, requirements, data relationships, and recommendations clearly.