15 days ago
Pune, IndiaMid Level
Responsibilities
- Build and maintain production data pipelines and models on Snowflake from ingestion through transformation and consumption.
- Develop tested and documented dbt models following team standards and governance practices.
- Build and support Apache Airflow DAGs that reliably orchestrate Snowflake workloads.
- Ingest enterprise data using Snowpipe, Snowpark, and dynamic tables.
- Apply data quality checks and monitor Snowflake warehouse cost and performance.
- Provide internal customer-zero feedback on Snowflake features to Product teams.
- Partner with stakeholders and senior engineers to convert requirements into scoped technical tasks.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
- At least 3 years of hands-on experience building production data pipelines or data models.
- Strong SQL and working Python skills, including basic query tuning and schema design.
- Hands-on experience with a cloud data warehouse, ideally Snowflake.
- Working knowledge of dbt and an orchestration tool such as Apache Airflow.
- Understanding of dimensional modeling concepts.
- Clear written and verbal communication and a collaborative, curious approach to learning.
- Ownership mindset with the ability to follow problems through to resolution.
- Preferred experience includes Snowflake Cortex AI functions, LLM APIs, ML training data or feature engineering, Kafka, Snowpipe Streaming, AWS, Azure, GCP, infrastructure-as-code, data contracts, or schema registry tooling.
Tech Stack
Categories
Data Engineering
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.