Data Engineer – Databricks & Lakehouse (Power BI Environment)

Remote Full TimeIndia (Remote)Jobgether

Role Overview

This role offers the opportunity to work on a modern, enterprise-scale data platform built on a Lakehouse architecture using Databricks. You will design and optimize scalable data pipelines that transform raw enterprise data into trusted, business-ready datasets powering analytics and Power BI reporting.

What You Will Do

Design, build, and maintain scalable data pipelines using Databricks, Spark, and Delta Lake within a Lakehouse architecture. Develop and manage bronze, silver, and gold layer transformations, ensuring optimized performance, reliability, and cost efficiency.

Why It Might Be a Fit

This is a high-impact role suited for an experienced data engineer passionate about scalable architecture, data quality, and modern analytics platforms. You will play a key role in shaping robust data models aligned with standardized business definitions and enterprise metrics.

Requirements

  • 8+ years of experience in data engineering or related roles within large-scale enterprise environments
  • Strong hands-on experience with Databricks, Apache Spark, and Delta Lake
  • Advanced proficiency in SQL and Python for data processing and pipeline development
  • Experience working with Power BI or similar BI and visualization tools
  • Solid understanding of data modeling, business logic translation, and enterprise data architecture
  • Experience with cloud data platforms such as Azure Data Factory, Synapse, or data lake environments
  • Familiarity with CI/CD pipelines, DevOps practices, and automated deployment workflows
  • Strong analytical and problem-solving skills with the ability to work cross-functionally with technical and business teams

Benefits

  • Remote opportunity based in India
  • Work on a modern Databricks Lakehouse architecture at enterprise scale
  • Exposure to advanced analytics and Power BI-driven reporting environments
  • Opportunity to work with large, complex, global data ecosystems
  • Collaborative, innovation-driven engineering environment
  • Involvement in AI-assisted data engineering and automation initiatives (where applicable)
  • Competitive compensation aligned with experience and market standards

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