We're hiring a Data Engineer with deep Snowflake and SQL expertise to join our data team. You'll build and optimize data pipelines, modernize orchestration, and bring order to complex data environments. Our current stack centers on Snowflake and Azure, but you'll work across a range of platforms and tools as projects demand - so we're looking for someone adaptable, who picks up unfamiliar systems fast and isn't tied to a single ecosystem.

What You'll Do

- Design, build, and optimize data pipelines on Snowflake and other platforms as needed
- Modernize and manage orchestration (Snowflake Tasks, Task Graphs, Streams, and other tools)
- Write and refactor complex transformation logic in SQL
- Interpret and re-engineer existing pipelines, including undocumented ones
- Maintain high-quality technical documentation: source-to-target mappings, dependency diagrams, runbooks
- Ensure data quality, parity, and performance across pipelines
- Adapt to varied cloud environments, tooling, and client stacks

Must-Have Skills

- SQL - advanced: complex joins, window functions, CTEs, query optimization
- Snowflake - hands-on with warehouses, roles, data modeling, and native orchestration (Tasks, Task Graphs, Streams)
- Data modeling - dimensional modeling, star/snowflake schemas, SCDs




- ETL/ELT - building and maintaining ingestion and transformation pipelines
- Python - for scripting, data processing, and pipeline automation
- Cloud data platforms - hands-on with at least one major cloud (Azure preferred; AWS/GCP valued)
- Version control - Git-based workflows
- Adaptability - proven ability to learn current tools, platforms, and undocumented systems quickly
- Reverse engineering - track record untangling undocumented or poorly documented ETL pipelines
- Documentation - source-to-target mappings, dependency diagrams, runbooks

Strongly Preferred

- Azure data stack - ADLS Gen2, Azure Data Factory
- Informatica IICS (or comparable Informatica)
- dbt - transformation tooling and testing
- Orchestration tools - Airflow, ADF pipelines
- CI/CD for data - automated pipeline deployment
- Streaming - Kafka, Snowpipe, or equivalent
- Other warehouses - Big Query, Redshift, Databricks

Qualifications

- Experience: 3-6 years in data engineering (adjust to your level: 3+ for mid, 6+ for senior)
- Education: Bachelor's in Computer Science, Engineering, Information Systems, or equivalent practical experience
- Certifications (a plus): Snow Pro Core/Advanced, Azure Data Engineer Associate (DP-203)

📌 Data Engineer (India)
🏢 Networth
📍 India