Senior Data Engineer

Overview

Senior Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization.

What you'll do

• Build, maintain, and optimize data pipelines for batch and streaming workloads.
• Develop reliable data models and transformations to support analytics, reporting, and operational use cases.
• Integrate new data sources, APIs, and event streams into the platform.
• Implement data quality checks, testing, documentation, and monitoring.
• Write clean, performant SQL and Python code.
• Contribute to improving performance, scalability, and cost-efficiency across the data platform.
• Work closely with other senior engineers and architects to implement architectural patterns and best practices.
• Collaborate with analysts and data scientists to translate requirements into technical solutions.
• Participate in code reviews, design discussions, and continuous improvement initiatives.
• Help maintain clear documentation of data flows, models, and processes.
• Support the adoption and roll-out of new data tools, standards, and workflows.
• Contribute to DataOps processes such as CI/CD, testing, and automation.
• Assist in monitoring pipeline health and resolving data-related issues.

What you'll need

• 6-10 years of experience as a Data Engineer or similar role.
• Hands-on experience with Snowflake (mandatory), including SQL, modeling, and basic optimization.
• Strong knowledge of current architecture best practices of data pipelines and DWH.
• Experience with dbt (or similar), including model development, tests, documentation, and version control workflows.
• Strong SQL skills for data modeling and analysis.
• Proficiency with Python for pipeline development and automation.
• Experience working with orchestration tools (Airflow, Dagster, Prefect, or equivalent).
• Understanding of ETL/ELT design patterns, data lifecycle, and data modeling best practices.
• Familiarity with cloud environments (AWS, GCP, or Azure).
• Knowledge of data quality, observability, or monitoring concepts.
• Good communication skills and the ability to collaborate with cross-functional teams.

Nice to have

• Exposure to streaming/event technologies (Kafka, Kinesis, Pub/Sub).
• Experience with data governance or cataloging tools.
• Basic understanding of ML workflows or MLOps concepts.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation).
• Familiarity with testing frameworks or data validation tools.

Details

• Employment type: Full-time.
• Work mode: Hybrid, with an requirement to work on average 2 days per week from an HPE office.
• Location: Bengaluru, Karnātaka, India.