Data Quality Engineer

Data Quality Engineer

Location: Montgomery, AL (Onsite)

Duration: 4 Months+ (This will be a long-term, one-year renewable) (Contract Hire)

In summary: A Data Quality Engineer, strong data analyst with deep technical skills in SQL, Purview, Data Pipelines and Data Modeling, plus experience in cloud data environments, automated testing, and collaboration with analytics and engineering teams. Ensures data is not only clean but also ready to support advanced analytics and AI applications

Data Quality Engineer & Analytics Skills

Core Technical Skills: MUST BE ABLE TO NAVIGATE AN ENVIRONMENT WITH LOW\NO DATA MATURITY

Data Profiling & Cleansing: Analyze data to identify anomalies, duplicates, outliers, and missing values; apply cleansing techniques to improve data integrity.

SQL Proficiency: Write complex queries to validate data accuracy, perform transformations, and generate reports. (SSIS - ETL\ELT)

Python & Other Languages: Python is widely used for automation, data validation, and integration with analytics pipelines; SQL is essential for querying and reporting.

Data Modeling & Warehousing: Understand ETL/ELT processes, data warehouse/lake/lakehouse architectures, and data modeling principles.

Cloud & Modern Data Stack: Experience with cloud platforms (AWS, Google Cloud Platform, Azure), modern data warehouses (Snowflake, BigQuery), and tools like Spark, Kafka/Kinesis, Hadoop, or S3.

Data Testing & Observability: Design and deploy automated data testing at scale; use observability platforms for real-time monitoring.

Analytics & Data Science Skills

Data Quality Standards & Metrics: Define and enforce data quality benchmarks; measure completeness, accuracy, timeliness, and consistency.

Root Cause Analysis: Identify why data issues occur (ETL bugs, user input errors, system failures) and implement fixes.

Collaboration with Data Scientists: Work with ML/data science teams to ensure training data is clean and reliable.

Statistical & Trend Analysis: Interpret patterns in large datasets to inform quality improvements.

Soft & Communication Skills

Stakeholder Engagement: Gather requirements from business, engineering, and analytics teams; advocate for data quality across the organization.

Problem-Solving & Attention to Detail: Spot and resolve data issues efficiently; maintain high precision in validation.

Documentation: Record quality issues, processes, and improvements for transparency and compliance.

Tools & Platforms

Query & Analysis: SQL, Python, Spark, Kafka/Kinesis, Hadoop, S3.

Data Quality Tools: Data profiling tools (MS Purview), validation scripts, observability platforms.

Collaboration: Jira, Snowflake, or other data governance platforms.

Required Skills:

• Strong experience working in low or immature data environments, establishing data quality processes from scratch (8-10 Years)
• Advanced SQL expertise for complex querying, data validation, and transformation (8-10 Years)
• Hands-on experience with ETL/ELT pipelines (e.g., SSIS or similar tools) (8-10 Years)
• Proficiency in Python for data automation, validation, and pipeline integration (5-8 Years)
• Experience with data profiling and cleansing (anomalies, duplicates, outliers, missing values) (8-10 Years)
• Solid understanding of data modeling and data warehouse/lake/lakehouse architectures (8-10 Years)
• Experience implementing data quality frameworks and metrics (accuracy, completeness, timeliness, consistency) (8-10 Years)
• Experience with cloud data platforms (AWS, Azure, or Google Cloud Platform) and modern data warehouses (e.g., Snowflake, BigQuery) (5-8 Years)
• Required Tools & Platforms: (8-10 Years) Query & Analysis: SQL, Python, Spark, Kafka/Kinesis, Hadoop, S3. Data Quality Tools: Data profiling tools (MS Purview), validation scripts, observability platforms. Collaboration: Jira, Snowflake, or other data governance platforms.
• Bachelor's Degree

Preferred Skills:

• Knowledge of DAMA-DMBoK, DCAM, MDM concepts, and governance frameworks. (8-10 Years)
• Experience with Microsoft Purview, Fabric, MS Power BI, and Key Vault (5-8 Years)
• Familiarity with AI/ML data readiness and feature-store-aligned data structuring. (5-8 Years)
• Cloud data engineering exposure (Azure, Databricks, Google Cloud Platform). (5-8 Years)
• Master's degree preferred.
• DAMA CDMP (Associate/Practitioner) EDM Council DCAM ASQ Data Quality Credential Collibra Data Steward Certification Certified Data Steward (eLearningCurve) Cloud/AI certifications (Azure, Databricks, Google)