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 recent 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 - BigQuery, 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): SnowPro Core/Advanced, Azure Data Engineer Associate (DP-203)

📌 Data Engineer (Karnataka)
🏢 Networth
📍 Karnataka