Job Requirements

JD for Data Engineer -

Core Data Engineering: 5+ years of experience designing, building, and optimizing robust, scalable data pipelines (ETL/ELT).

Programming Languages: 4+ years of strong, production-grade coding experience in Python and advanced proficiency writing complex, optimized SQL queries.

Big Data Technologies: 3+ years of experience working with distributed computing frameworks such as Apache Spark (or PySpark).

Data Warehousing & Architecture: Advanced proficiency in cloud data platforms (such as Snowflake, Google BigQuery, or AWS Redshift)

Problem-Solving & Data Quality: Proven track record of implementing rigorous data validation, data cleansing, and error-handling frameworks

Cloud Infrastructure: 2+ years of hands-on experience with cloud platforms (AWS, GCP, or Azure), particularly with managed data services and serverless computing.

Orchestration Tools: Experience managing and monitoring complex workflows using tools like Apache Airflow, Prefect, or dbt.

CI/CD & DevOps: Experience deploying pipelines via CI/CD frameworks (GitHub Actions, GitLab CI, or Jenkins) and infrastructure-as-code (Terraform)

Please List 3-5 Primary Responsibilities For This Role

Pipeline Development: Design, develop, and maintain automated,



scalable ETL/ELT pipelines to ingest, transform, and load high-volume data from disparate sources

Data Modeling & Optimization: Build and optimize clean, reliable relational and dimensional data models to support self-service analytics and business intelligence workloads

Data Value Chains: A robust understanding of modern software value chain models and cross-functional data mapping

Agile Frameworks: Experience operating within Scrum or Kanban frameworks with a solid grasp of sprint delivery and cross-functional dependencies

AI Skills: All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to

Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise

Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows

Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery quality and velocity.

Work Experience

25years

📌 Data Engineer (Bengaluru)
🏢 Virtusa
📍 Bengaluru