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Data Engineer

Zensar Technologies • IT Consulting & Digital Transformation • IT Outsourcing & Dedicated Teams • Python • SQL • 3 days ago
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Location
In office (Hyderabad)

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Sep 22, 2026. Zensar Technologies scores A on the Alion truth index.

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Zensar Technologies

Zensar Technologies is an Indian technology services company founded in 1991 and part of the RPG Group, providing digital engineering, application services and infrastructure management to clients in banking, insurance, retail, manufacturing and healthcare. Its work covers experience design, cloud and data engineering, application development and maintenance, and increasingly applied artificial intelligence, delivered from centres in India, South Africa, Europe and the United States. Headquartered in Pune and listed in India, it earns a large share of revenue from North America and has been repositioning toward higher-value engineering work.
zensar.com • HQ: Pune, India • IT Outsourcing & Dedicated Teams • IT Consulting & Digital Transformation • 1001-5000 employees • Est. 1991
Tech stack of Zensar Technologies

Responsibilities

  • Design, build, and maintain data pipelines and engineering workflows
  • Develop and optimize solutions using SQL, Python, and Databricks
  • Independently manage and deliver tasks assigned from the project backlog
  • Support initiatives involving:
    • Data ingestion, Process automation, transformation
    • Data quality assurance and validation
    • Data and system integration
  • Ensure data accuracy, reliability, and operational efficiency across workflows

Follow best practices in data engineering, documentation, and quality control

Responsibilities

  • Design, build, and maintain data pipelines and engineering workflows
  • Develop and optimize solutions using SQL, Python, and Databricks
  • Independently manage and deliver tasks assigned from the project backlog
  • Support initiatives involving:
    • Data ingestion, Process automation, transformation
    • Data quality assurance and validation
    • Data and system integration
  • Ensure data accuracy, reliability, and operational efficiency across workflows

Follow best practices in data engineering, documentation, and quality control

.

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