TCS: Virtual Interview :Data Analyst/Engineer || 26th June

V2 Solutions; Ernakulam; On-site; INR 900,000 - 1,500,000; Full time

Job summary

V2 Solutions is seeking a Data Analyst/Engineer in India to design and maintain data pipelines using PySpark and Databricks. The role focuses on analytics-enabled data transformations and scalable data workflows across diverse sources.

Responsibilities include building ETL/ELT pipelines, optimizing Spark jobs for performance and cost, and collaborating with architects and analysts to deliver datasets for BI and reporting needs.

Qualifications

• 5–9 years of experience in data engineering or analytics.
• Experience building ETL/ELT data pipelines with PySpark.
• Experience with Databricks and Spark optimization.
• Experience with multiple data sources (S3, APIs) and AWS services.
• Strong data transformation, cleansing, and enrichment skills.
• Ability to collaborate with architects and analysts.

Responsibilities

• Develop ETL/ELT data pipelines using PySpark/Spark leveraging lake flow declarative pipelines.
• Perform data transformation, cleansing, and enrichment.
• Optimize Spark jobs for performance and cost efficiency.
• Build datasets for analytics, reporting, and BI consumption.
• Leverage AWS query federation SDK/Databricks Data source API for query federation.
• Work with multiple data sources (S3, APIs, etc.).
• Support data validation, reconciliation, and testing.
• Collaborate with architects, analysts, and downstream teams.

Skills

PySpark

Databricks

Spark

ETL/ELT

Data pipelines

Data transformation

AWS

Query federation

S3

APIs

Tools

Databricks

Spark

Job description

Role: Data Analyst/Engineer Exp Range: 5-9 years Location: Chennai, Kochi, Trivandrum Virtual Interview Date: 26-June-26 Job description Responsible for building and maintaining data pipelines and transformation logic using PySpark/Databricks, enabling analytics and reporting use cases.

Roles & Responsibilities

• Develop ETL/ELT data pipelines using PySpark/Spark leveraging lake flow declarative pipelines
• Perform data transformation, cleansing, and enrichment
• Optimize Spark jobs for performance and cost efficiency
• Build datasets for analytics, reporting, and BI consumption
• Leverage AWS query federation SDK/Databricks Data source API for query federation
• Work with multiple data sources (S3, APIs, etc.)
• 1 Support data validation, reconciliation, and testing
• Collaborate with architects, analysts, and downstream teams