Data Engineer

Role Summary:

We are seeking a Data Engineer with strong expertise in Databricks and large-scale data engineering to design, build, and optimize our data lakehouse solutions. The ideal candidate will be hands-on with PySpark, SQL, Delta Lake, and cloud data platforms, and will play a key role in enabling data-driven decision-making across the organization.

Key Responsibilities

• Design, develop, and maintain

  scalable ETL/ELT pipelines

  using Databricks and PySpark.
• Implement and optimize data ingestion from multiple structured and unstructured sources (APIs, databases, streaming data, cloud storage).
• Build and manage

  Delta Lake

  architectures for data lakehouse solutions.
• Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data sets for analytics and ML models.
• Ensure

  data quality, governance, and security compliance

  across the data lifecycle.
• Optimize Databricks workloads for performance, cost efficiency, and scalability.
• Implement CI/CD pipelines for data engineering workflows using tools like Git, Azure DevOps, or Jenkins.
• Monitor, troubleshoot, and resolve issues in data pipelines and platform operations.

Skill & Qualification:

• 5 - 8 years of experience in Data Engineering, with minimum 3+ years in Databricks.
• Expert in PySpark, SQL, Delta Lake, and advanced Databricks features.
• Strong experience with at least one cloud platform (Azure, AWS, or GCP) and its data services (e.g., ADF, Glue, BigQuery, Redshift, Synapse).
• Hands-on experience in ETL/ELT, data modeling, and performance tuning.
• Proficiency in Git, DevOps practices, and CI/CD workflows.
• Solid understanding of data security, lineage, and governance frameworks.