IT - Workforce - Data Quality Engineer

Job Responsibilities

• 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 – ETLETL).
• Python & Other
  
  Languages:
  
  Use Python 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, GCP, Azure), modern data warehouses (Snowflake, Big Query), 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.
• 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.

• Strong SQL and Python skills.
• Experience with data profiling tools (e.g., MS Purview) and validation scripts.
• Knowledge of observability platforms and automated testing frameworks.
• Understanding of data governance platforms (e.g., Jira, Snowflake).
• Excellent stakeholder engagement, problem‑solving, and meticulous attention to detail.
• Ability to work in a low/no data maturity environment.

Candidate will work 100% in Montgomery, AL. Remote work is not allowed.

Standard background check required.