Security Comments :
Standard Background Check Temporary Worker Agreement Confirmation of E-Verify Completion Resource Employment Status Validation Form
Position Description :
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, GCP, 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. 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 - Resumes Forwarded: As resumes are received - Contract Type: Convert to Merit - Contract Length: 9/30/26 with extension possibility. This will be a long-term, one-year renewable that will eventually convert to merit. - BR: BR - Max # of Submissions: 2 - Submission Timeline: 6/5 5pm CT - Interview Types: Video or In person based on location - Interview Timeline: As resumes are reviewed - Background Requirements: Standard background check - Target Start Date: 6/15/26 - Submittals must include: Resume, RTR, and Cover letter - Candidate will work 100% in Montgomery AL - No remote work allowed - Submittals must be from a prime supplier. Suppliers can use only one (1) level of sub-vendor candidates for submittal if necessary. - This role does not offer sponsorship Confirmation needed if candidate is eligible for conversion to Merit.
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