Data Engineering Lead

Summary

Lead Data Engineer at IndusInd Bank in Mumbai, architecting and building a cloud-native Azure Databricks data platform with PySpark, leading a team of data engineers and analysts to deliver scalable lakehouse architectures and BI solutions.

Greetings from IndusInd Bank!!


We are currently hiring for Lead Data Engineer

Location- Mumbai

Experience- 10 to 17 Yrs


Role Details:

Lead, design, and build a cloud-native data platform on Azure, with a strong focus on Azure Databricks and modern data engineering practices. This role requires a hands-on technical leader who can architect scalable solutions while leading a team of Data Engineers and Data Analysts to deliver high-quality data products and insights.

Overall, Job Description


Data Modeling & Architecture

  • Expertise in:
  • Conceptual, Logical & Physical Data Modeling
  • Dimensional modeling (Star/Snowflake schemas)
  • Experience building models optimized for:
  • Lakehouse (Databricks)
  • Analytical workloads
  • Strong understanding of:
  • Data partitioning, indexing, data skipping, caching techniques


Programming & Transformation

  • Strong hands-on skills in:
  • PySpark (mandatory)
  • Python for data transformation and automation
  • Experience implementing:
  • Data pipelines using Databricks workflows / jobs
  • Reusable transformation frameworks


Integration & Advanced Capabilities

  • Experience with:
  • API integration and data ingestion frameworks
  • CI/CD in Azure (Azure DevOps, Git integration with Databricks)
  • Infrastructure as Code (ARM templates / Terraform – good to have)


Analytics & BI

  • Experience integrating data platforms with:
  • Power BI (preferred)
  • Ability to:
  • Create semantic layers and datasets for business consumption
  • Enable self-service analytics


Good to Have

  • Knowledge of:
  • Azure Purview / Microsoft Fabric (emerging tools)
  • Data governance frameworks
  • Streaming analytics and real-time pipelines


Key Responsibilities

  • Hands-On Technical Leadership
  • Lead by example with active hands-on development in Azure Databricks
  • Design and implement:
  • Scalable lakehouse architectures
  • High-performance data pipelines
  • Review and guide code, architecture, and best practices across the team


Team Leadership

  • Manage and mentor a team of:
  • Data Engineers
  • Data Analysts
  • Drive best practices in:
  • Code quality
  • Data engineering standards
  • Agile delivery


Architecture & Solution Design

  • Translate business needs into:
  • End-to-end Azure data architecture
  • Scalable and reusable data models
  • Define standards for:
  • Data ingestion, transformation, storage, and serving layers


Stakeholder Engagement

  • Collaborate with business stakeholders to:
  • Understand requirements
  • Translate into technical solutions
  • Deliver actionable insights
  • Act as the primary data advisor for the business vertical


Data Governance & Quality

  • Implement and enforce:
  • Data governance policies
  • Data quality frameworks
  • Metadata management (cataloging, lineage)


Optimization & Innovation

  • Continuously optimize:
  • Databricks jobs (performance & cost)
  • Data storage and query performance
  • Stay current with:
  • Azure and Databricks advancements
  • Modern data engineering practices (Medallion architecture, DataOps)

Education and Work Experience Requirements:

EDUCATION

Essential requirements:

  • Bachelor’s degree in Computer Science or equivalent
  • Preferred certifications:
  • Azure Data Engineer (DP-203)
  • Azure Fundamentals (AZ-900)
  • Databricks certifications (Associate/Professional)


Preferred: Technical Skills

Core Data Engineering (Must-Have)

  • Deep expertise in Azure Data Platform, including:
  • Azure Databricks (core focus) – Spark (PySpark/Scala), Delta Lake, notebooks, workflow orchestration
  • Azure Data Factory (ADF) – pipeline orchestration, integration runtime, monitoring
  • Azure Data Lake (ADLS Gen2) – storage design, partitioning strategies
  • Azure Synapse Analytics – data warehousing and SQL analytics
  • Strong hands-on experience with:
  • Data lakehouse architecture (Delta Lake preferred)
  • ETL/ELT frameworks and data ingestion patterns
  • Streaming (good to have): Structured Streaming, Event Hub, Kafka

WORK EXPERIENCE

  • 10–17 years of overall experience
  • Minimum 7+ years of hands-on experience in:
  • Data engineering
  • Data modeling in large-scale enterprise data platforms
  • Strong experience in Azure-based data ecosystems and Databricks implementations
  • Proven track record of leading data engineering and analytics teams


Interested candidates kindly share your updated profile on shraddha.mankuskar@indusind.com or apply on our ATS portal link below for better tracking:

Requition No: 87628

https://app1100.workline.hr/CandidatePortal/18999c40-0af4-47be-88b8-e594e8d8af58/Data-Warehousing-Engineer-Job-in-IBL-House-Office-86846

See also

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