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At ADROSONIC, we are looking for a highly skilled Senior Data Engineer - Snowflake with 5+ years of hands-on experience in designing, building, and optimizing modern cloud-native data platforms. The ideal candidate will bring strong expertise in Snowflake architecture, cloud data services, and advanced data modelling practices. The ideal candidate will bring deep expertise in Snowflake architecture, advanced ELT design, Snowpark-based development, performance tuning, and data modelling best practices. This role requires strong ownership of Snowflake environments, enabling high-concurrency analytics workloads, cost-efficient compute management, and seamless BI enablement. You will play a key role in building scalable Snowflake data warehouse architectures, enabling seamless integration with BI tools, and ensuring efficient, secure, and optimized ELT pipelines across projects. Requirements Key Responsibilities Snowflake Data Platform Development Design and implement scalable Snowflake architectures leveraging multi-cluster Virtual Warehouses. Configure and manage auto-scaling, auto-suspend/resume, and workload isolation strategies. Implement Snowpipe for continuous ingestion and leverage Streams & Tasks for CDC-based incremental processing. Utilize Time Travel and Fail-safe for audit, recovery, and governance requirements. Implement Zero-Copy Cloning for environment management (Dev / UAT / Prod). Design secure access models using RBAC, Dynamic Data Masking, Row Access Policies, and Secure Views. Optimize separation of storage and compute for cost and concurrency efficiency. Data Modeling & Warehousing: Design conceptual, logical, and physical data models. Implement dimensional modeling techniques (Star Schema, Snowflake Schema). Develop well-structured fact and dimension tables optimized for analytical workloads. Ensure data models are optimized for BI tools such as Power BI, Tableau, or similar platforms. Maintain consistency, scalability, and performance across evolving data models. Design data models using different database schemas (Kimball, Star, Snowflake) for optimal data retrieval and storage. Ensure models are optimized for both transactional (OLTP) and analytical (OLAP) workloads, using best practices in database design. Performance Engineering & Cost Optimization: Analyse query profiles and execution plans to identify bottlenecks. Optimize micro-partition pruning and clustering depth. Implement warehouse sizing strategies for workload segmentation (BI vs batch). Monitor warehouse utilization and resource monitors proactively. Leverage Materialized Views and Search Optimization Service where applicable. Continuously optimize compute cost while maintaining performance SLAs. ETL Development, Snowpark & Data Integration: Develop modular and scalable ELT pipelines using Snowflake-native SQL transformations. Implement CDC-based incremental loading using Streams & Tasks. Develop advanced transformations using Snowpark (Python/Scala/Java) where required. Create and manage UDFs and Stored Procedures using SQL, JavaScript, and Snowpark. Integrate Snowflake with cloud storage platforms (Azure Data Lake, AWS S3, Blob Storage). Design external stages and file formats for structured and semi-structured data ingestion. Handle semi-structured data using VARIANT data types, JSON parsing, and Snowflake-native functions. Work with dbt for modular transformation orchestration and scalable modeling layers. BI Collaboration & Analytical Enablement: Work closely with BI developers to design analytics-ready datasets within Snowflake. Ensure seamless integration between Snowflake and BI tools such as Power BI, Tableau, or Looker. Support backend optimization to improve dashboard performance. Act as a technical bridge between Data Engineering and BI teams. Stakeholder & Client Collaboration Collaborate seamlessly with internal stakeholders and external clients. Translate complex business requirements into scalable Snowflake-based solutions. Gather, analyze, and translate business requirements into scalable technical solutions. Clearly communicate data architecture decisions, pipeline designs, and modeling approaches. Participate in client workshops, technical discussions, and solution presentations. Ensure strong alignment between business objectives and delivered data solutions. Performance Optimization & Reliability Monitor warehouse utilization and query performance proactively. Optimize compute usage, clustering, partitioning, and caching strategies. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.