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Data Engineer - GCH

Zensar Technologies • IT Consulting & Digital Transformation • IT Outsourcing & Dedicated Teams • Python • SQL • Power BI • Azure Data Factory • Dimensional Modeling • 7 days ago
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Salary
≈ $21k – $42k per year (Estimated)
Location
In office (India)
Seniority
Senior · 7+ years exp

Confirmed on the employer's own hiring board on Oct 1, 2026. First seen by Alion on Sep 25, 2026. Zensar Technologies scores A on the Alion truth index.

Overview
Company
Impact
Profile match

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

1. Data Engineer:

Core Skills and Knowledge:

- 7-9 years of experience in data engineering.

- Hands-on experience with Databricks (Delta Lake, notebooks, Databricks Workflows).

- Strong programming skills in Python and SQL.

- Working knowledge of Apache Spark (PySpark preferred) for data transformation.

- Experience with either AWS (S3, Glue, EMR) or Azure (ADLS Gen2, Data Factory, Synapse) services.

- Understanding of data modeling concepts (star schema, dimensional modeling).

- Familiarity with version control (Git) and basic CI/CD practices.

Desirable Skills:

- Databricks Certified Data Engineer Associate (or working toward it).

- Exposure to streaming data pipelines (Structured Streaming, Kafka, Kinesis, or Event Hubs).

- Familiarity with orchestration tools (Airflow, Databricks Workflows).

- Basic understanding of data governance and security practices (PII masking, access control).

- Exposure to Power BI or other BI tools for understanding downstream

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