Senior Data Engineer - Snowflake

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.

Key Responsibilities:

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. Performance Optimization & Reliability
   

·        Monitor warehouse utilization and query performance proactively.

·        Optimize compute usage, clustering, partitioning, and caching strategies.

·        Implement logging, monitoring, and alerting mechanisms.

·        Ensure high availability, reliability, and secure data access.

·        Continuously improve scalability and maintainability of Snowflake environments.

1. Best Practices & Standards
   

·        Follow data engineering standards, naming conventions, and documentation practices.

·        Implement version control and CI/CD processes for data pipelines.

·        Promote reusable SQL components, clean coding practices and Modular ELT design.

·        Ensure adherence to security standards, RBAC implementation and basic data governance principles.

1. Performance Optimization & Maintenance:
   

·        Continuously monitor and optimize the performance of Snowflake data models, making improvements to ensure efficiency and scalability.

·        Conduct regular audits to ensure architecture alignment with evolving data strategy and business needs.

·        Conduct periodic architecture reviews and performance audits.

·        Ensure alignment of Snowflake architecture with evolving enterprise data strategy.

·        Evaluate and implement new Snowflake features and capabilities proactively.

Required Qualifications:

·        Bachelor’s/Master’s degree in Computer Science, Data Science, Information Systems, or a related field.

·        5+ years of hands-on experience in Data Engineering with strong Snowflake implementation experience.

·        Strong expertise in Snowflake architecture including Virtual Warehouses, Snowpipe, Streams, Tasks, Time Travel, Zero-Copy Cloning, RBAC, and Query Profiling.

·        Mandatory strong experience in Data Modeling (conceptual, logical, physical) with solid knowledge of dimensional modeling and data warehousing principles.

·        Advanced proficiency in SQL, including performance optimization and complex transformations; experience with NoSQL databases is a plus.

·        Hands-on experience designing ELT pipelines using Snowflake and orchestration tools (ADF, Airflow, dbt, etc.).

·        Experience developing ELT pipelines using Snowflake-native SQL and Snowpark (Python/Scala/Java).

·        Experience building UDFs and Stored Procedures using SQL, JavaScript, or Snowpark.

·        Experience handling semi-structured data using Snowflake VARIANT data types and JSON functions.

·        Good understanding of data governance fundamentals, data quality, metadata management, and compliance standards.

·        Strong analytical, troubleshooting, and stakeholder collaboration skills with experience working in Agile delivery environments and version-controlled setups.

Preferred Qualifications:

·        Snowflake SnowPro Core or SnowPro Advanced certification preferred

·        Domain experience in Insurance and Financial Services is a strong plus.

·        Experience in client-facing or consulting environments.

·        Exposure to DevOps practices and Git-based version control.

·        Experience handling enterprise-scale data environments.

Soft Skills:

·        Strong collaborative mindset.

·        Seamless coordination with BI teams and stakeholders.

·        Strong client communication and stakeholder management skills.

·        Ownership-driven and proactive approach.

·        Strong analytical and problem-solving abilities.

·        Ability to work effectively in fast-paced, evolving environments.