Job Description
Job Title:  DataBricks - Data Engineering
City:  Pune
State/Province:  Maharashtra
Posting Start Date:  10/21/26
Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com.
Job Description: 

Job Description

Required Skills and Experience

  • Hands-on experience developing data engineering solutions on Databricks.
  • Strong proficiency in Python, PySpark, and SQL, including complex transformations and query optimization.
  • Working knowledge of Apache Spark execution, partitioning, joins, caching, and performance troubleshooting.
  • Experience with Delta Lake, including merge operations, schema management, and transactional data processing.
  • Experience building ETL/ELT pipelines, implementing incremental loads, and applying data warehousing and dimensional modeling concepts.
  • Experience with at least one cloud platform—Azure, AWS, or Google Cloud—and its storage and identity services.
  • Working knowledge of Databricks jobs, notebooks, compute, Unity Catalog, and access management.
  • Proficiency with Git, code reviews, automated testing, and deployment practices.
  • Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

Key Responsibilities

  • Design, build, and maintain batch and streaming data pipelines using Databricks, PySpark, Python and SQL.
  • Ingest and integrate data from databases, files, APIs, and cloud storage into a lakehouse architecture.
  • Develop bronze, silver, and gold data layers using Delta Lake and implement incremental processing, deduplication, and change data capture where required.
  • Optimize Spark workloads, SQL queries, compute configurations, and storage layouts for performance and cost efficiency.
  • Schedule and orchestrate data workflows; implement monitoring, alerting, error handling, and recovery mechanisms.
  • Implement data validation, reconciliation, automated testing, and data quality controls.
  • Apply data governance and access controls using Unity Catalog and organizational security standards.
  • Collaborate with architects, analysts, and business stakeholders to translate requirements into technical solutions.
  • Maintain version-controlled code, support CI/CD processes, and document pipelines, data models, and operational procedures.
  • Troubleshoot production issues and contribute to root-cause analysis and preventive improvements.

͏

Required Skills and Experience

  • Hands-on experience developing data engineering solutions on Databricks.
  • Strong proficiency in Python, PySpark, and SQL, including complex transformations and query optimization.
  • Working knowledge of Apache Spark execution, partitioning, joins, caching, and performance troubleshooting.
  • Experience with Delta Lake, including merge operations, schema management, and transactional data processing.
  • Experience building ETL/ELT pipelines, implementing incremental loads, and applying data warehousing and dimensional modeling concepts.
  • Experience with at least one cloud platform—Azure, AWS, or Google Cloud—and its storage and identity services.
  • Working knowledge of Databricks jobs, notebooks, compute, Unity Catalog, and access management.
  • Proficiency with Git, code reviews, automated testing, and deployment practices.
  • Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

Key Responsibilities

  • Design, build, and maintain batch and streaming data pipelines using Databricks, PySpark, Python and SQL.
  • Ingest and integrate data from databases, files, APIs, and cloud storage into a lakehouse architecture.
  • Develop bronze, silver, and gold data layers using Delta Lake and implement incremental processing, deduplication, and change data capture where required.
  • Optimize Spark workloads, SQL queries, compute configurations, and storage layouts for performance and cost efficiency.
  • Schedule and orchestrate data workflows; implement monitoring, alerting, error handling, and recovery mechanisms.
  • Implement data validation, reconciliation, automated testing, and data quality controls.
  • Apply data governance and access controls using Unity Catalog and organizational security standards.
  • Collaborate with architects, analysts, and business stakeholders to translate requirements into technical solutions.
  • Maintain version-controlled code, support CI/CD processes, and document pipelines, data models, and operational procedures.
  • Troubleshoot production issues and contribute to root-cause analysis and preventive improvements.

͏

Required Skills and Experience

  • Hands-on experience developing data engineering solutions on Databricks.
  • Strong proficiency in Python, PySpark, and SQL, including complex transformations and query optimization.
  • Working knowledge of Apache Spark execution, partitioning, joins, caching, and performance troubleshooting.
  • Experience with Delta Lake, including merge operations, schema management, and transactional data processing.
  • Experience building ETL/ELT pipelines, implementing incremental loads, and applying data warehousing and dimensional modeling concepts.
  • Experience with at least one cloud platform—Azure, AWS, or Google Cloud—and its storage and identity services.
  • Working knowledge of Databricks jobs, notebooks, compute, Unity Catalog, and access management.
  • Proficiency with Git, code reviews, automated testing, and deployment practices.
  • Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

Key Responsibilities

  • Design, build, and maintain batch and streaming data pipelines using Databricks, PySpark, Python and SQL.
  • Ingest and integrate data from databases, files, APIs, and cloud storage into a lakehouse architecture.
  • Develop bronze, silver, and gold data layers using Delta Lake and implement incremental processing, deduplication, and change data capture where required.
  • Optimize Spark workloads, SQL queries, compute configurations, and storage layouts for performance and cost efficiency.
  • Schedule and orchestrate data workflows; implement monitoring, alerting, error handling, and recovery mechanisms.
  • Implement data validation, reconciliation, automated testing, and data quality controls.
  • Apply data governance and access controls using Unity Catalog and organizational security standards.
  • Collaborate with architects, analysts, and business stakeholders to translate requirements into technical solutions.
  • Maintain version-controlled code, support CI/CD processes, and document pipelines, data models, and operational procedures.
  • Troubleshoot production issues and contribute to root-cause analysis and preventive improvements.

͏

Required Skills and Experience

  • Hands-on experience developing data engineering solutions on Databricks.
  • Strong proficiency in Python, PySpark, and SQL, including complex transformations and query optimization.
  • Working knowledge of Apache Spark execution, partitioning, joins, caching, and performance troubleshooting.
  • Experience with Delta Lake, including merge operations, schema management, and transactional data processing.
  • Experience building ETL/ELT pipelines, implementing incremental loads, and applying data warehousing and dimensional modeling concepts.
  • Experience with at least one cloud platform—Azure, AWS, or Google Cloud—and its storage and identity services.
  • Working knowledge of Databricks jobs, notebooks, compute, Unity Catalog, and access management.
  • Proficiency with Git, code reviews, automated testing, and deployment practices.
  • Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

Key Responsibilities

  • Design, build, and maintain batch and streaming data pipelines using Databricks, PySpark, Python and SQL.
  • Ingest and integrate data from databases, files, APIs, and cloud storage into a lakehouse architecture.
  • Develop bronze, silver, and gold data layers using Delta Lake and implement incremental processing, deduplication, and change data capture where required.
  • Optimize Spark workloads, SQL queries, compute configurations, and storage layouts for performance and cost efficiency.
  • Schedule and orchestrate data workflows; implement monitoring, alerting, error handling, and recovery mechanisms.
  • Implement data validation, reconciliation, automated testing, and data quality controls.
  • Apply data governance and access controls using Unity Catalog and organizational security standards.
  • Collaborate with architects, analysts, and business stakeholders to translate requirements into technical solutions.
  • Maintain version-controlled code, support CI/CD processes, and document pipelines, data models, and operational procedures.
  • Troubleshoot production issues and contribute to root-cause analysis and preventive improvements.

͏

Required Skills and Experience

  • Hands-on experience developing data engineering solutions on Databricks.
  • Strong proficiency in Python, PySpark, and SQL, including complex transformations and query optimization.
  • Working knowledge of Apache Spark execution, partitioning, joins, caching, and performance troubleshooting.
  • Experience with Delta Lake, including merge operations, schema management, and transactional data processing.
  • Experience building ETL/ELT pipelines, implementing incremental loads, and applying data warehousing and dimensional modeling concepts.
  • Experience with at least one cloud platform—Azure, AWS, or Google Cloud—and its storage and identity services.
  • Working knowledge of Databricks jobs, notebooks, compute, Unity Catalog, and access management.
  • Proficiency with Git, code reviews, automated testing, and deployment practices.
  • Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

Key Responsibilities

  • Design, build, and maintain batch and streaming data pipelines using Databricks, PySpark, Python and SQL.
  • Ingest and integrate data from databases, files, APIs, and cloud storage into a lakehouse architecture.
  • Develop bronze, silver, and gold data layers using Delta Lake and implement incremental processing, deduplication, and change data capture where required.
  • Optimize Spark workloads, SQL queries, compute configurations, and storage layouts for performance and cost efficiency.
  • Schedule and orchestrate data workflows; implement monitoring, alerting, error handling, and recovery mechanisms.
  • Implement data validation, reconciliation, automated testing, and data quality controls.
  • Apply data governance and access controls using Unity Catalog and organizational security standards.
  • Collaborate with architects, analysts, and business stakeholders to translate requirements into technical solutions.
  • Maintain version-controlled code, support CI/CD processes, and document pipelines, data models, and operational procedures.
  • Troubleshoot production issues and contribute to root-cause analysis and preventive improvements.

 

 

Mandatory Skills: DataBricks - Data Engineering .

 

Experience: 5-8 Years .

 

Reinvent your world. We are building a modern Wipro. We are an end-to-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA - as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention.
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