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Strong PwC brand, metro location, and senior mid-level data-quality focus create moderate applicant density.
Requires specific data platform, data quality, and cloud experience, limiting cross-industry transferability.
Explicit 8–15 years plus mandatory testing, SQL and data-platform experience makes filtering stringent.
Lead end-to-end quality engineering for enterprise data platforms including data warehouses and data lakes.
Design, execute, and manage comprehensive testing activities across manual and automated tests covering data pipelines, APIs, and integrations with technologies like Apache Spark, Kafka, and Kinesis.
Collaborate with cross-functional teams to define quality standards, perform data validation using SQL/MySQL, support releases in Agile environments, and mentor junior team members.
8-15 years of professional experience in data quality engineering or related areas.
Bachelor's degree in Technology, Engineering, or MCA (B.Tech, M.Tech, M.E, MCA, B.E).
Strong skills in testing, test design, and defect tracking; experience with Python or PySpark preferred.
Experience with data platforms on cloud environments such as AWS or Azure; familiarity with services like S3, Redshift, Glue, Athena, EMR, DMS, Airflow is advantageous.
Experienced in large-scale enterprise data environments involving data lakes, warehouses, and advanced data processing frameworks.
Skilled in collaborative Agile delivery with strong ownership to meet release timelines and quality metrics.
Comfortable working closely with both business and technical stakeholders to translate requirements into test strategies and validations.