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Lead Data Engineer

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Onsite

Lead Data Engineer

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Lead Data Engineer

Skills

Google Cloud PlatformSQL QueriesBig DataData EngineeringPythonJavaSnowflakeDatabricksGitData AnalysisScalaAWSPySparkAzure

11 days ago

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Important information


Contract type:

Permanent

Salary:

₹22 – ₹28 / year

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

9 October 2026

What they need


We are Hiring! Lead Data Engineer | Pune - Onsite

Location: Pune (Full-time)

Experience: 8+ Years

Skills: Python, SQL, Databricks, Spark/Pyspark, ETL

Role Overview:

As a Lead Data Engineer, you will drive the design and development of scalable batch & streaming data pipelines, work with modern Lakehouse/Data Warehouse platforms, and partner with analytics and business teams to enable data-driven decision-making.

Key Responsibilities:

  • Data Pipeline Development: Design, build, and maintain high-performance batch and streaming pipelines using PySpark/Scala and Databricks .
  • Data Modeling & Architecture: Design efficient schemas and curated datasets optimized for speed, scalability, and cost across Lakehouse/Data Warehouse platforms.
  • Data Integration: Seamlessly integrate data from APIs, databases, flat files, and streaming sources across cloud platforms.
  • Performance Optimization: Identify bottlenecks, tune Spark configurations, and optimize queries for large-scale data processing.
  • Data Quality & Governance: Establish data validation checks and quality standards to maintain reliable data platforms.
  • Collaboration & Leadership: Partner with Data Scientists, Analysts, and Cloud Engineers while setting technical best practices, version control, and CI/CD standards.

Key Qualifications & Requirements:

  • Education: Bachelor's degree in Computer Science, Engineering, or a related field.
  • Experience: 8+ years of hands-on experience in Data Engineering.
  • Technical Skills:
  • Strong expertise in Python, Scala, or Java and high proficiency in SQL .
  • Deep experience with Apache Spark (PySpark/Scala) and Databricks .
  • Familiarity with cloud platforms ( AWS, Azure, or GCP ) and data warehouses ( Snowflake, Redshift, etc. ).
  • Sound knowledge of data modeling principles, ETL design patterns, and big data frameworks.

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