Senior Data Engineer (Snowflake)

Location: On-site in Pune, India

Company

Snowflake provides a data platform for building data-driven enterprise capabilities and internal decision-making systems.

What you will do

• Design, build, and launch production-ready data models and pipelines from ingestion through transformation, modeling, and consumption.
• Own end-to-end system design decisions, evaluate tradeoffs, and document architectural choices.
• Implement data governance frameworks and maintain rigorous data quality standards.
• Develop and optimize ingestion processes from diverse enterprise data sources.
• Build tools for the data platform, align with the product roadmap, and provide feedback as an internal customer.
• Lead testing strategies, requirements gathering, Agile data product development, and stakeholder alignment.

Requirements

• Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
• 5–8 years of hands-on experience building and operating production data pipelines, data models, and platform infrastructure at scale.
• Expert SQL and strong Python skills, including performance tuning, query optimization, and schema design for large-scale analytical workloads.
• Hands-on Snowflake experience, including Snowpark, dynamic tables, data sharing, Snowflake Cortex, and cost optimization.
• Deep expertise in dbt and strong proficiency with Apache Airflow, including advanced modeling, testing, macro authoring, DAG design, and operational practices.
• Strong system design, architectural reasoning, communication, ownership, and ambiguity-management skills.

Nice to have

• Experience designing data pipelines and feature engineering workflows for machine learning training and inference.
• Exposure to MLflow, feature stores, vector databases, or LLM serving infrastructure.
• Experience with Snowflake Cortex AI functions or similar LLM API integrations.
• Knowledge of AWS, Azure, or GCP, infrastructure as code, streaming ingestion, Kafka, Snowpipe Streaming, data contracts, or schema registries.

Culture & Benefits

• AI-native, experimental approach to improving how work gets done.
• Fast-moving environment focused on challenging conventional thinking and accelerating innovation.
• Collaborative culture emphasizing low-ego problem-solving, ownership, and raising team standards.
• Work includes collaboration with stakeholders from executive to operational levels.