{"id":3989143,"url":"https://alion.io/job/adrosonic-senior-data-engineer-snowflake","title":"Senior Data Engineer - Snowflake","company":{"id":4529397,"name":"Adrosonic","domain":"adrosonic.com","url":"https://alion.io/company/adrosonic","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"JavaScript","optional":false},{"name":"Looker","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Agile","optional":true},{"name":"Git","optional":true}],"status":"live","first_seen_at":"2026-11-09T22:35:00Z","employer_posted_date":null,"last_verified_at":"2026-11-09T22:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"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.\n\nThe 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.\n\nYou 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.\n\nRequirements:\n\nKey Responsibilities:\nDesign and implement scalable Snowflake architectures leveraging multi-cluster Virtual Warehouses.\nConfigure and manage auto-scaling, auto-suspend/resume, and workload isolation strategies.\nImplement Snowpipe for continuous ingestion and leverage Streams & Tasks for CDC-based incremental processing.\nUtilize Time Travel and Fail-safe for audit, recovery, and governance requirements.\nImplement Zero-Copy Cloning for environment management (Dev / UAT / Prod).\nDesign secure access models using RBAC, Dynamic Data Masking, Row Access Policies, and Secure Views.\nOptimize separation of storage and compute for cost and concurrency efficiency.\n\nData Modeling & Warehousing:\nDesign conceptual, logical, and physical data models.\nImplement dimensional modeling techniques (Star Schema, Snowflake Schema).\nDevelop well-structured fact and dimension tables optimized for analytical workloads.\nEnsure data models are optimized for BI tools such as Power BI, Tableau, or similar platforms.\nMaintain consistency, scalability, and performance across evolving data models.\nDesign data models using different database schemas (Kimball, Star, Snowflake) for optimal data retrieval and storage.\nEnsure models are optimized for both transactional (OLTP) and analytical (OLAP) workloads, using best practices in database design.\n\nPerformance Engineering & Cost Optimization:\nAnalyze query profiles and execution plans to identify bottlenecks.\nOptimize micro-partition pruning and clustering depth.\nImplement warehouse sizing strategies for workload segmentation (BI vs batch).\nMonitor warehouse utilization and resource monitors proactively.\nLeverage Materialized Views and Search Optimization Service where applicable.\nContinuously optimize compute cost while maintaining performance SLAs.\n\nETL Development, Snowpark & Data Integration:\nDevelop modular and scalable ELT pipelines using Snowflake-native SQL transformations.\nImplement CDC-based incremental loading using Streams & Tasks.\nDevelop advanced transformations using Snowpark (Python/Scala/Java) where required.\nCreate and manage UDFs and Stored Procedures using SQL, JavaScript, and Snowpark.\nIntegrate Snowflake with cloud storage platforms (Azure Data Lake, AWS S3, Blob Storage).\nDesign external stages and file formats for structured and semi-structured data ingestion.\nHandle semi-structured data using VARIANT data types, JSON parsing, and Snowflake-native functions.\nWork with dbt for modular transformation orchestration and scalable modeling layers.\n\nBI Collaboration & Analytical Enablement:\nWork closely with BI developers to design analytics-ready datasets within Snowflake.\nEnsure seamless integration between Snowflake and BI tools such as Power BI, Tableau, or Looker.\nSupport backend optimization to improve dashboard performance.\nAct as a technical bridge between Data Engineering and BI teams.\n\nStakeholder & Client Collaboration:\nCollaborate seamlessly with internal stakeholders and external clients.\nTranslate complex business requirements into scalable Snowflake-based solutions.\nGather, analyze, and translate business requirements into scalable technical solutions.\nClearly communicate data architecture decisions, pipeline designs, and modeling approaches.\nParticipate in client workshops, technical discussions, and solution presentations.\nEnsure strong alignment between business objectives and delivered data solutions.\n\nPerformance Optimization & Reliability:\nMonitor warehouse utilization and query performance proactively.\nOptimize compute usage, clustering, partitioning, and caching strategies.\nImplement logging, monitoring, and alerting mechanisms.\nEnsure high availability, reliability, and secure data access.\nContinuously improve scalability and maintainability of Snowflake environments.\n\nBest Practices & Standards:\nFollow data engineering standards, naming conventions, and documentation practices.\nImplement version control and CI/CD processes for data pipelines.\nPromote reusable SQL components, clean coding practices and modular ELT design.\nEnsure adherence to security standards, RBAC implementation and basic data governance principles.\n\nPerformance Optimization & Maintenance:\nContinuously monitor and optimize the performance of Snowflake data models, making improvements to ensure efficiency and scalability.\nConduct regular audits to ensure architecture alignment with evolving data strategy and business needs.\nConduct periodic architecture reviews and performance audits.\nEnsure alignment of Snowflake architecture with evolving enterprise data strategy.\nEvaluate and implement new Snowflake features and capabilities proactively.\n\nRequired Qualifications:\nBachelor's/Master's degree in Computer Science, Data Science, Information Systems, or a related field.\n5+ years of hands-on experience in Data Engineering with strong Snowflake implementation experience.\nStrong expertise in Snowflake architecture including Virtual Warehouses, Snowpipe, Streams, Tasks, Time Travel, Zero-Copy Cloning, RBAC, and Query Profiling.\nMandatory strong experience in Data Modeling (conceptual, logical, physical) with solid knowledge of dimensional modeling and data warehousing principles.\nAdvanced proficiency in SQL, including performance optimization and complex transformations; experience with NoSQL databases is a plus.\nHands-on experience designing ELT pipelines using Snowflake and orchestration tools (ADF, Airflow, dbt, etc.).\nExperience developing ELT pipelines using Snowflake-native SQL and Snowpark (Python/Scala/Java).\nExperience building UDFs and Stored Procedures using SQL, JavaScript, or Snowpark.\nExperience handling semi-structured data using Snowflake VARIANT data types and JSON functions.\nGood understanding of data governance fundamentals, data quality, metadata management, and compliance standards.\nStrong analytical, troubleshooting, and stakeholder collaboration skills with experience working in Agile delivery environments and version-controlled setups.\n\nPreferred Qualifications:\nSnowflake SnowPro Core or SnowPro Advanced certification preferred\nDomain experience in Insurance and Financial Services is a strong plus.\nExperience in client-facing or consulting environments.\nExposure to DevOps practices and Git-based version control.\nExperience handling enterprise-scale data environments.\n\nSoft Skills:\nStrong collaborative mindset.\nSeamless coordination with BI teams and stakeholders.\nStrong client communication and stakeholder management skills.\nOwnership-driven and proactive approach.\nStrong analytical and problem-solving abilities.\nAbility to work effectively in fast-paced, evolving environments.\nSkills\nAirflow, DWH, Data Modeling, Dimensional Modeling, ELT, ETL, Snowflake, Snowpark, Warehousing, dbt","description_format":"text","description_chars":7654,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-11-09T22:35:00Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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