Data Quality Engineer

Overview

Job Role:

Data Quality Engineer
Duration: 03 months (Possibility of extension)

Location:

Montgomery, AL - 36130 (Onsite)
Interview:
Video or In person Based on Location

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.

• Navigate environments with low data maturity and establish data quality processes from scratch.
• Data profiling and 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).
• Develop automation using Python and other languages; query and report data using SQL.
• Data modeling and warehousing: understand ETL/ELT processes, data warehouse/lake/lakehouse architectures, and data modeling principles.
• Work with cloud platforms (AWS, GCP, Azure), modern data warehouses (Snowflake, Big Query), and tools like Spark, Kafka/Kinesis, Hadoop, or S3.
• Design and deploy automated data testing at scale; implement observability for real-time monitoring.

• Define and enforce data quality standards and metrics (completeness, accuracy, timeliness, consistency).
• Root cause analysis to identify why data issues occur and implement fixes.
• Collaborate with data scientists to ensure training data is clean and reliable.
• Statistical and trend analysis to inform quality improvements.

• Stakeholder engagement: gather requirements from business, engineering, and analytics teams; advocate for data quality across the organization.
• Problem-solving and attention to detail; maintain high precision in validation.
• Documentation of quality issues, processes, and improvements for transparency and compliance.

• 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.

• Strong experience 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 (8-10 years).
• Solid understanding of data modeling and data warehouse/lake/lakehouse architectures (8-10 years).
• Experience implementing data quality frameworks and metrics (8-10 years).
• Experience with cloud data platforms (AWS, Azure, or GCP) and modern data warehouses (e.g., Snowflake, Big Query) (5-8 years).
• Required Tools & Platforms: SQL, Python, Spark, Kafka/Kinesis, Hadoop, S3;
  Data quality tools: MS Purview, validation scripts, observability platforms;
  
  Collaboration:
  
  Jira, Snowflake, or other data governance platforms.
• Bachelor’s Degree.

• 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, GCP) (5-8 years).
• Master’s degree preferred.
• DAMA CDMP (Associate/Practitioner), EDM Council DCAM, ASQ Data Quality Credential, Collibra Data Steward Certification, eLearning
  
  Curve, cloud/AI certifications (Azure, Databricks, Google).