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 quick 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 new 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 (Bengaluru)
🏢 Networth
📍 Bengaluru