Description

You own data integrity and quality frameworks in low-maturity environments, ensuring data readiness for advanced analytics and AI. This role is on-site.

Responsibilities

• Establish data quality processes from scratch in environments with limited existing maturity.
• Profile and cleanse data to identify anomalies, duplicates, outliers, and missing values.
• Write complex SQL queries to validate accuracy, perform transformations, and generate reports.
• Design and deploy automated data testing at scale using observability platforms.
• Conduct root cause analysis for data issues and collaborate with engineering teams to implement fixes.

Required Skills

• 8-10+ years of experience working in low or immature data environments.
• Advanced SQL proficiency for complex querying, validation, and transformation.
• Hands-on experience with ETL/ELT pipelines, specifically SSIS.
• Proficiency in Python for data automation, validation, and pipeline integration (5-8+ years).
• Strong background in data modeling and warehouse/lake/lakehouse architectures.
• Experience implementing data quality frameworks and metrics (completeness, accuracy, timeliness).
• Experience with cloud data platforms (AWS, Azure, GCP) and modern warehouses like Snowflake or BigQuery.
• Familiarity with tools including Spark, Kafka/Kinesis, Hadoop, and S3.

Preferred Skills

• Knowledge of DAMA-DMBoK, DCAM, MDM concepts, and governance frameworks.
• Experience with Microsoft Purview, Fabric, and Power BI.

Key Skills

Education

Bachelor's degree