Manager – Data Architect, Data Analytics, Advisory

Overview

Manager-level role in Data Quality Engineering for enterprise data applications and data platforms, focusing on end-to-end quality engineering, testing, and release validation in Agile environments.

What you'll do

• Lead end-to-end quality engineering for enterprise data applications, data platforms, data warehouses, and data lakes.
• Design and execute testing across planning, test design, execution, defect tracking, release validation, and quality reporting.
• Perform manual, functional, system, integration, regression, UI, API, and end-to-end testing.
• Validate source-to-target data, transformations, business rules, completeness/accuracy, and reconciliation across data pipelines.
• Validate data processing built with Apache Spark and test inputs/outputs from streaming or queue-based integrations (e.g., Kafka, Kinesis).
• Create reusable test scenarios, test cases, test data, and regression suites to improve coverage and efficiency.
• Use tools like Postman for API validation and JIRA/Spira for defect lifecycle management and reporting.

What you'll need

• 8–10 years of experience (as stated in the responsibilities section).
• Mandatory skills in Testing, Test Design, and Defect Tracking.
• Experience/working knowledge of SQL and relational databases such as MySQL.
• Familiarity with data lake and data warehouse concepts including storage, security, cataloging, architecture patterns, data modelling, and star schemas.
• Practical exposure to cloud data platforms on AWS or Azure; services like S3, Redshift, Glue, Athena, EMR, DMS, Airflow, or equivalent are advantageous (as stated).
• Basic programming knowledge in Python or PySpark for technical troubleshooting collaboration.
• Education in B.Tech/M.Tech/M.E/MCA/B.E (with specific degree list provided).