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Data Engineer (Data Retention Project)

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Onsite

Data Engineer (Data Retention Project)

Xforia Technologies

Data Engineer (Data Retention Project)

Skills

SQL QueriesDatabricksGitData AnalysisAWSPySparkApache Airflow

1 month ago

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Important information


Contract type:

Permanent

Salary:

₹2,000,000 – ₹3,500,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

11 September 2026

What they need


Job Details

Data Engineer (Data Retention Project)

Bengaluru

5 Days Onsite

Shift Timing - 02:00 PM to 11:00 PM

Job Description

Role & Responsibilities:

  • Design and maintain scalable data pipelines for data ingestion, transformation, and processing.
  • Build and optimize ETL/ELT workflows using PySpark, Databricks, SQL, and Airflow.
  • Develop data models and solutions for retention, reporting, and analytics needs.
  • Ensure data quality, integrity, and reliability through validation and monitoring frameworks.
  • Integrate data from multiple sources and optimize processing performance.
  • Collaborate with cross-functional teams to deliver data solutions and resolve data issues.
  • Maintain technical documentation, data lineage, and mapping artifacts.
  • Support production deployments, troubleshooting, and continuous improvement through engineering best practices.

Technical Skills Requirements:

  1. PySpark & Spark SQL: Hands-on experience writing production-grade PySpark code
  2. Databricks: Proven experience working within Databricks environments, including cluster configuration and Delta Lake
  3. Apache Airflow: Strong experience building and managing pipelines in Apache Airflow
  4. SQL Mastery: Advanced SQL skills (window functions, CTEs, query optimization, indexing strategies)
  5. Data Modeling: Solid understanding of modern data warehousing concepts
  6. Version Control: Proficiency with Git and CI/CD deployment workflows
  7. AWS: Experience in Amazon Web Services (AWS) cloud infrastructure
  8. ETL/ELT: Proven track record of designing, building, and optimizing scalable ETL/ELT pipelines and data platforms

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