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 DoDesign, build, and optimize data pipelines on Snowflake and other platforms as neededModernize and manage orchestration (Snowflake Tasks, Task Graphs, Streams, and other tools)Write and refactor complex transformation logic in SQLInterpret and re-engineer existing pipelines, including undocumented onesMaintain high-quality technical documentation: source-to-target mappings, dependency diagrams, runbooksEnsure data quality, parity, and performance across pipelinesAdapt to diverse cloud environments, tooling, and client stacksMust-Have SkillsSQL - advanced: complex joins, window functions, CTEs, query optimizationSnowflake - hands-on with warehouses, roles, data modeling, and native orchestration (Tasks, Task Graphs, Streams)Data modeling - dimensional modeling, star/snowflake schemas,



SCDsETL/ELT - building and maintaining ingestion and transformation pipelinesPython - for scripting, data processing, and pipeline automationCloud data platforms - hands-on with at least one major cloud (Azure preferred; AWS/GCP valued)Version control - Git-based workflowsAdaptability - proven ability to learn new tools, platforms, and undocumented systems quicklyReverse engineering - track record untangling undocumented or poorly documented ETL pipelinesDocumentation - source-to-target mappings, dependency diagrams, runbooksStrongly PreferredAzure data stack - ADLS Gen2, Azure Data FactoryInformatica IICS (or comparable Informatica)dbt - transformation tooling and testingOrchestration tools - Airflow, ADF pipelinesCI/CD for data - automated pipeline deploymentStreaming - Kafka, Snowpipe, or equivalentOther warehouses - BigQuery, Redshift, DatabricksQualificationsExperience: 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 experienceCertifications (a plus): SnowPro Core/Advanced, Azure Data Engineer Associate (DP-203)

📌 Data Engineer (Bengaluru)
🏢 Networth
📍 Bengaluru