Data Quality Analyst

TEKsystems•Montgomery, AL

2d•$35 - $60•Onsite

About The Position

Establish, advance, and mature data quality and governance capabilities in a green‑field, low‑maturity data environment. Support enterprise analytics, BI, and AI/ML readiness through SQL/ETL engineering, data profiling, validation, stewardship, metadata management, and early‑stage data architecture. Drive long‑term improvement of data standards, definitions, lineage, and quality processes.

Requirements

8-10 years of Data Quality & Engineering experience.
Advanced T‑SQL, SQL Server development, debugging, and performance tuning.
SSIS development, deployment, and troubleshooting.
Data profiling, validation rule design, quality scoring, and measurement techniques.
ETL/ELT pipeline design, debugging, and optimization.
Data modeling (conceptual, logical, physical).
Metadata management and lineage documentation.
Reporting and dashboarding with Power BI, Tableau, or similar tools.
Strong documentation and communication skills.

Nice To Haves

Knowledge of DAMA‑DMBoK, DCAM, MDM concepts, and governance frameworks.
Experience in low‑maturity/green‑field data environments.
Familiarity with AI/ML data readiness and feature‑store‑aligned data structuring.
Cloud data engineering exposure (Azure, Databricks, GCP).

Responsibilities

Perform data audits, profiling, validation, anomaly detection, and quality gap identification.
Develop automated data quality rules and validation logic using T‑SQL, SQL Server, stored procedures, and indexing strategies.
Build and maintain SSIS packages for validation, cleansing, transformation, and error‑detection workflows.
Troubleshoot ETL/ELT pipelines, data migrations, integration failures, and data load issues.
Conduct root‑cause analysis and implement preventive and long‑term remediation solutions.
Optimize SQL queries, tune stored procedures, and improve data processing performance.
Document audit findings, validation processes, data flows, standards, and quality reports.
Build dashboards and reports for data quality KPIs using Power BI/Tableau.
Define, maintain, and enforce data quality standards, business rules, data definitions, and governance policies.
Monitor datasets for completeness, accuracy, timeliness, consistency, and compliance.
Ensure proper and consistent data usage across departments and systems.
Maintain business glossaries, data dictionaries, data dictionaries, metadata repositories, and lineage documentation.
Partner with IT, data engineering, and business teams to support governance initiatives and compliance requirements.
Provide training on data entry, data handling, stewardship practices, and data literacy.
Collaborate with cross‑functional teams to identify recurring data issues and recommend preventive solutions.
Architect initial data quality frameworks, validation layers, governance artifacts, and ingestion patterns.
Establish scalable data preparation workflows supporting analytics, BI, and AI/ML readiness.
Mature data quality and governance processes from ad‑hoc to standardized, automated, and measurable.
Drive adoption of data quality and governance practices across business and technical teams.
Support long‑term evolution of enterprise data strategy and governance maturity.