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Data Engineering offshore

Zensar Technologies · IT Consulting & Digital Transformation · IT Outsourcing & Dedicated Teams · Python · SQL · Power BI · ETL/ELT · SSIS · 3 days ago

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Location

In office (Pune)

Seniority

Senior · 6+ years exp

Visa

Sponsors visas in United States, United Kingdom; no register of this country names it

Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Nov 4, 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

As a Senior Data Engineer, you will:

Design, develop, and support scalable batch and near real-time data pipelines, data products, and lakehouse-ready data assets using Spark, Python, SQL, Hive, Databricks, and cloud-native technologies.
Build and optimize capabilities supporting the ONE Data Lake and Mastercard's unified data platform strategy.
Develop ingestion, transformation, and orchestration solutions for batch and streaming/near real-time data patterns using technologies such as Cribl, SSIS, Apache Ozone, Cloudera Data Services, Hive, Spark, Azure, Microsoft Fabric, Databricks, and related ecosystem tools.
Enable trusted, governed, and accessible enterprise data through the implementation of data quality, lineage, and governance standards.
Partner closely with Product, TPM, Data Governance, Architect, and Engineering teams to deliver strategic business outcomes.
Support AI, analytics, reporting, and decisioning use cases by delivering scalable and reliable data assets.
Drive modernization initiatives across hybrid data environments and cloud.
Improve operational efficiency through automation, monitoring, observability, and self-service capabilities.
Troubleshoot complex platform and data issues, perform root cause analysis, and implement sustainable solutions.
Evaluate emerging technologies and identify opportunities to advance Team ONE's data capabilities.

Required Qualifications

6+ years of experience delivering enterprise-scale data engineering, data warehousing, or big data solutions.
Strong hands-on experience with Apache Spark, Python, SQL and Databricks.
Experience building and maintaining large-scale data pipelines and distributed data processing systems.
Experience with streaming or near real-time ingestion patterns, including event/log data collection, routing, transformation, and delivery into enterprise data lake or lakehouse platforms.
Experience with Hive, Apache Ozone, or similar data integration, Azure, Microsoft Fabric, and orchestration technologies.
Strong understanding of data architecture, ETL/ELT design patterns, and modern data platform principles.
Hands-on experience with Microsoft SQL Server, SSIS packages, stored procedures, SQL performance tuning, and relational database concepts.
Experience implementing data quality, governance, metadata, and lineage capabilities.
Proven ability to analyze complex production issues, perform root cause analysis, and implement long-term solutions.
Strong communication and collaboration skills with the ability to work effectively across technical and business teams.
Demonstrated ability to manage competing priorities in a fast-paced environment.
Strong ownership mindset and commitment to delivering high-quality solutions.

As a Senior Data Engineer, you will:

Design, develop, and support scalable batch and near real-time data pipelines, data products, and lakehouse-ready data assets using Spark, Python, SQL, Hive, Databricks, and cloud-native technologies.
Build and optimize capabilities supporting the ONE Data Lake and Mastercard's unified data platform strategy.
Develop ingestion, transformation, and orchestration solutions for batch and streaming/near real-time data patterns using technologies such as Cribl, SSIS, Apache Ozone, Cloudera Data Services, Hive, Spark, Azure, Microsoft Fabric, Databricks, and related ecosystem tools.
Enable trusted, governed, and accessible enterprise data through the implementation of data quality, lineage, and governance standards.
Partner closely with Product, TPM, Data Governance, Architect, and Engineering teams to deliver strategic business outcomes.
Support AI, analytics, reporting, and decisioning use cases by delivering scalable and reliable data assets.
Drive modernization initiatives across hybrid data environments and cloud.
Improve operational efficiency through automation, monitoring, observability, and self-service capabilities.
Troubleshoot complex platform and data issues, perform root cause analysis, and implement sustainable solutions.
Evaluate emerging technologies and identify opportunities to advance Team ONE's data capabilities.

Required Qualifications

6+ years of experience delivering enterprise-scale data engineering, data warehousing, or big data solutions.
Strong hands-on experience with Apache Spark, Python, SQL and Databricks.
Experience building and maintaining large-scale data pipelines and distributed data processing systems.
Experience with streaming or near real-time ingestion patterns, including event/log data collection, routing, transformation, and delivery into enterprise data lake or lakehouse platforms.
Experience with Hive, Apache Ozone, or similar data integration, Azure, Microsoft Fabric, and orchestration technologies.
Strong understanding of data architecture, ETL/ELT design patterns, and modern data platform principles.
Hands-on experience with Microsoft SQL Server, SSIS packages, stored procedures, SQL performance tuning, and relational database concepts.
Experience implementing data quality, governance, metadata, and lineage capabilities.
Proven ability to analyze complex production issues, perform root cause analysis, and implement long-term solutions.
Strong communication and collaboration skills with the ability to work effectively across technical and business teams.
Demonstrated ability to manage competing priorities in a fast-paced environment.
Strong ownership mindset and commitment to delivering high-quality solutions.

Required Qualifications

6+ years of experience delivering enterprise-scale data engineering, data warehousing, or big data solutions.
Strong hands-on experience with Apache Spark, Python, SQL and Databricks.
Experience building and maintaining large-scale data pipelines and distributed data processing systems.
Experience with streaming or near real-time ingestion patterns, including event/log data collection, routing, transformation, and delivery into enterprise data lake or lakehouse platforms.
Experience with Hive, Apache Ozone, or similar data integration, Azure, Microsoft Fabric, and orchestration technologies.
Strong understanding of data architecture, ETL/ELT design patterns, and modern data platform principles.
Hands-on experience with Microsoft SQL Server, SSIS packages, stored procedures, SQL performance tuning, and relational database concepts.
Experience implementing data quality, governance, metadata, and lineage capabilities.
Proven ability to analyze complex production issues, perform root cause analysis, and implement long-term solutions.
Strong communication and collaboration skills with the ability to work effectively across technical and business teams.
Demonstrated ability to manage competing priorities in a fast-paced environment.
Strong ownership mindset and commitment to delivering high-quality solutions.

Preferred Qualifications

Experience with Cribl or similar observability/log streaming platforms, including data routing, filtering, enrichment, and delivery to downstream lakehouse or analytics platforms.
Experience with Microsoft Fabric, OneLake, and cloud-native data platforms.
Experience with enterprise data governance and metadata management solutions.
Experience supporting analytics, reporting, AI, or machine learning workloads.
Experience with Power BI and enterprise reporting environments.
Experience mentoring engineers and leading technical initiatives.

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