IN_Senior Associate_Data Engineer Databricks_GCC_Advisory_Gurgaon

Price Waterhouse Cooper LLP; Gurugram District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Price Waterhouse Cooper LLP is seeking an experienced Data Engineer – Databricks to design, build, and operate scalable data pipelines on the Databricks Lakehouse Platform. You will work hands-on with Apache Spark, Databricks notebooks, Delta Lake, and cloud-native services in collaboration with analytics, AI/ML, and business teams.

The role requires 3+ years of data engineering experience with Databricks, strong SQL, and exposure to Azure/AWS/GCP.

Qualifications

• 3+ years of Data Engineer experience with Databricks
• Hands-on Spark, PySpark, and Spark SQL
• Delta Lake and Lakehouse architecture knowledge
• Advanced SQL skills
• Cloud exposure: Azure / AWS / GCP
• Experience in Agile teams
• Data governance and data quality awareness

Responsibilities

• Design, build, and maintain end-to-end data pipelines on Databricks (PySpark/Spark SQL)
• Develop and optimize Databricks notebooks, jobs, and workflows
• Ingest, transform, and curate large-scale structured and semi-structured datasets
• Collaborate with data scientists, analysts, and architects on AI/ML workloads
• Ensure data quality, reliability, lineage, and governance
• Provide production support and document data flows and runbooks
• Mentor junior engineers and contribute to best practices

Skills

Databricks

Apache Spark

PySpark

Spark SQL

Delta Lake

SQL

ETL/ELT

Cloud platforms

Agile

Data governance

Python

Education

Bachelor's in Computer Science or Engineering

MBA

Tools

Azure

AWS

GCP

Unity Catalog

Auto Loader

CI/CD

Python

Databricks

Job description

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.

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

We are seeking an experienced Data Engineer – Databricks to design, build, and operate scalable, high-performance data pipelines on the Databricks Lakehouse Platform. The role involves hands-on development using Apache Spark, Databricks notebooks, Delta Lake, and cloud-native services, along with close collaboration with analytics, AI/ML, and business teams

Responsibilities

• Design, build, and maintain end-to-end data pipelines using Databricks (PySpark / Spark SQL)
• Implement batch and incremental data processing using Delta Lake and multi-hop architecture
• Develop and optimize Databricks notebooks, jobs, and workflows
• Ingest, transform, and curate large-scale structured and semi-structured datasets
• Support analytics, reporting, and downstream data consumption use cases
• Ensure data quality, reliability, lineage, and governance
• Collaborate with data scientists, analysts, and architects on AI/ML workloads
• Optimize Spark jobs for performance and cost efficiency
• Adhere to enterprise security, access control, and compliance standards
• Provide production support and troubleshoot data pipeline issues
• Document technical designs, data flows, and operational runbooks
• Mentor junior engineers and contribute to best practices

Mandatory skill sets

• 3+ years of experience as a Data Engineer with strong Databricks expertise
• Hands-on experience with Apache Spark, PySpark, and Spark SQL
• Strong knowledge of Delta Lake and Lakehouse architecture
• Advanced SQL skills
• Experience with ETL/ELT patterns and data warehousing concepts
• Exposure to at least one cloud platform (Azure / AWS / GCP)
• Understanding of distributed computing concepts
• Experience working in Agile teams

Preferred skill sets

• Experience with Unity Catalog and data governance
• Exposure to Auto Loader or streaming frameworks
• CI/CD for data pipelines
• Python for data engineering and automation
• Databricks certification (Associate / Professional)

Years of experience required

3 to 8 years

Education qualification

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (60% above)

Education

Degrees/Field of Study required: Master of Business Administration, Bachelor of Engineering

Degrees/Field of Study preferred:

Required Skills

Data Engineering

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, AI Fluency, AI-Human Collaboration, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake {+ 30 more}