IN_Manager_Data Architect_Data Analytics_Advisory_Chennai

PwC

Chennai

On-site

INR 4,000,000 - 7,000,000

Full time

14 days+

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Job summary

PwC India is seeking a Senior Data Quality Engineer for its Data, Analytics & AI practice in Chennai. You will lead end-to-end quality engineering for enterprise data applications, data platforms, warehouses, and lakes, partnering with stakeholders to ensure robust data pipelines and reliable releases.

You will validate data, design test coverage, and mentor junior team members while working in an Agile environment with exposure to Spark, Kafka, and cloud platforms such as AWS or Azure.

Qualifications

8-15 years of data engineering and QA testing experience.
Bachelor/BE at minimum, relevant tech degrees.
Experience with AWS/Azure cloud data platforms is preferred.

Responsibilities

Lead end-to-end quality engineering for enterprise data apps, warehouses and lakes.
Define quality standards with stakeholders and design test coverage.
Test data pipelines, validate source-to-target data.
Validate Spark jobs and streaming inputs from Kafka/Kinesis.
Create reusable test scenarios, data sets, and regression suites.
Collaborate with cross-functional teams to prevent defects.
Guide junior team members and promote quality practices.
Support releases with evidence for approvals.

Skills

Testing

Test Design

Defect Tracking

Education

B.Tech / M.Tech / M.E / MCA

Bachelor of Technology, Bachelor of Engineering

Tools

Azure Service Fabric

Data Engineering

Job description

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Manager

Job Description & Summary

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.

In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

Why PWC

At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes forour clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences foreach other. Learn more about us.

Equal Employment Opportunities Statement

At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.

Job Description & Summary:

A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.

Responsibilities:

Senior Data Quality Engineer

As a Senior Data Quality Engineer with 8–10 years of experience, you will lead end-to-end quality engineering for enterprise data applications, data platforms, data warehouses, and data lakes.
You will work closely with business stakeholders, product owners, developers, and data engineering teams to understand requirements, define quality standards, design effective test coverage, validate data pipelines, and support reliable releases in Agile delivery environments.
Lead testing activities 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 for enterprise and data applications.
Validate source-to-target data, transformations, business rules, completeness, accuracy, and reconciliation across data pipelines.
Test data processing built with Apache Spark and validate input and output data from streaming or queue-based integrations such as Kafka and Kinesis.
Create reusable test scenarios, test cases, test data, and regression suites to improve coverage and delivery efficiency.
Use Postman for API validation and tools such as JIRA or Spira for defect lifecycle management, traceability, and reporting.
Participate in requirement reviews and functional design discussions, perform test impact analysis, and define acceptance and quality criteria.
Collaborate with clients, product owners, developers, and cross-functional teams to prevent defects and resolve issues within release timelines.
Support production releases by validating functional and non-functional requirements and providing evidence for release approvals.
Work effectively in Agile teams, contribute to ceremonies and sprint quality updates, and deliver testing within iterative release cycles.
Guide junior team members, review testing outputs, share knowledge, and promote consistent quality engineering practices.
Apply working knowledge of SQL and relational databases such as MySQL to perform data validation and investigation.
Understand data lake and data warehouse concepts, including storage, security, cataloging, architecture patterns, data modelling, and star schemas.
Have practical exposure to cloud data platforms on AWS or Azure; familiarity with services such as S3, Redshift, Glue, Athena, EMR, DMS, Airflow, or equivalent services is advantageous.
Demonstrate basic programming knowledge in Python or PySpark and collaborate effectively with data engineering teams on technical troubleshooting.
Communicate clearly with technical and business stakeholders, manage multiple work tracks, and demonstrate strong ownership, interpersonal skills, and continuous learning.
Exposure to test automation, reporting tools such as Power BI or QuickSight, emerging technologies, Generative AI, or relevant data and quality certifications is an added advantage.