Data Quality Analyst / Engineer (Banking SAS + Data Governance)

Title: Data Quality Analyst / Engineer (Banking SAS + Data Governance)
Location: Charlotte, NC(Remote)
Duration: Long Term Contract

Overview

Our client is supporting a leading financial services client on a high-priority data quality initiative focused on scaling rule development, improving data governance integration, and stabilizing enterprise data platforms. We are seeking experienced Data Quality Analysts / Engineers with strong SAS and data governance experience to join a fast-moving team addressing critical data quality and integration challenges.

Key Responsibilities

• Develop, test, and deploy data quality rules in SAS across large, complex datasets.
  Analyse and profile data to identify quality issues, anomalies, and gaps.
  Translate business/data requirements into DQ rules, validations, and monitoring logic.
  Support integration of data quality outputs into data governance platforms (e.g., Alation, Manta, Collibra).
  Partner with data stewards, engineers, and business teams to define and refine data quality standards.
  Document rules, logic, and lineage to support auditability and regulatory requirements.
  Assist in transitioning from manual rule creation to more automated and scalable frameworks.
  Support remediation efforts and ongoing monitoring of data quality KPIs.

Required Experience

• 5+ years in data quality, data engineering, or data governance roles.
  Strong hands-on experience with SAS (Base SAS, SAS Data Management, or similar).
  Experience writing and implementing data quality rules at scale.
  Background working in financial services (banking preferred).
  Familiarity with data governance / lineage tools (Alation, Manta, Collibra, or similar).
  Strong SQL skills and experience working with large enterprise datasets.
  Experience working in regulated environments with audit and compliance considerations.

Preferred Experience

• Exposure to Snowflake or modern cloud data platforms.
  Experience integrating data quality into downstream workflows / reporting systems.
  Familiarity with data lineage and metadata management concepts.
  Experience working in hybrid environments (legacy + modern data stacks).

Key Traits for Success

• Able to operate in ambiguous, fast-moving environments.
  Strong problem solver with attention to detail.
  Comfortable working directly with stakeholders and translating needs into technical solutions.
  Bias toward execution and delivery over theory.