Snowflake Engineer

We are looking for a Snowflake Engineer who can design, build, and operate Snowflake-based data solutions across the broader cloud data ecosystem. This is not a SQL-only development role. The ideal candidate should bring strong Snowflake engineering depth along with awareness of hyperscaler services, DevOps practices, automation, security, observability, and AI-enabled data workloads. The engineer will contribute to production-grade pipelines, platform automation, performance optimisation, governance, and AI-ready data foundations.

The candidate will have responsibilities across the following functions:

Snowflake Platform Engineering

• Design, develop, and operate scalable Snowflake-based data solutions for ingestion, transformation, modelling, analytics, and AI-ready data consumption.
• Build Snowflake-native pipelines using external stages, file formats, Snowpipe, Streams, Tasks, Dynamic Tables where applicable, and idempotent processing patterns.
• Develop and manage Snowflake databases, schemas, tables, views, stored procedures, secure views, and reusable transformation components.
• Work with semi-structured data using Snowflake capabilities such as VARIANT, OBJECT, ARRAY, FLATTEN, and related SQL constructs.

Performance, Reliability and Cost Optimisation:

• Tune Snowflake workloads using query profiles, warehouse sizing, clustering considerations, caching behaviour, and SQL optimisation techniques.
• Monitor and manage Snowflake credit consumption through warehouse configuration, auto-suspend/auto-resume, workload separation, and resource monitors.
• Build production-ready pipelines with failure handling, restartability, data reconciliation, monitoring, and operational support practices.
• Troubleshoot performance issues, pipeline failures, data freshness gaps, and production incidents in collaboration with platform and application teams.

Cloud, DevOps and Ecosystem Awareness:

• Work with Snowflake in the context of AWS, Azure, or GCP services including object storage, IAM, networking, secrets management, eventing, and monitoring.
• Understand how cloud storage, external stages, private connectivity, identity, and access controls integrate with Snowflake.
• Support CI/CD-based deployment of Snowflake objects, SQL scripts, dbt models, pipeline code, and configuration changes across environments.
• Use Git-based version control, branching, pull requests, code reviews, and environment promotion practices.
• Collaborate with DevOps and platform teams on automation, deployment reliability, monitoring, and operational support.

AI, Analytics and Data Product Awareness:

• Understand how governed, well-modelled Snowflake data supports analytics, AI, ML, semantic models, and agentic workloads.
• Exposure to Snowflake Cortex, Snowpark, vector search, semantic models, or AI-assisted analytics is preferred.
• Collaborate with analytics, data science, and AI teams to prepare reliable, secure, explainable, and reusable data foundations.
• Apply responsible data handling practices for AI use cases, including access control, metadata, lineage, and data quality considerations.

Collaboration and Support:

• Collaborate with architects, business analysts, data scientists, platform engineers, DevOps teams, and application teams to translate requirements into reliable Snowflake solutions.
• Participate in design reviews, code reviews, sprint planning, release readiness, and production support discussions.
• Document technical designs, operational runbooks, data flows, deployment steps, and support procedures.
• Contribute to engineering standards, reusable patterns, automation, and continuous improvement across Snowflake delivery.

Requirements:

• 4-8 years of experience in data engineering, cloud data platforms, data warehousing, or related engineering roles.
• 2+ years of hands-on experience with Snowflake in development, platform engineering, or production support contexts.
• Strong SQL is required, but the role is not limited to SQL development; candidates must demonstrate awareness of cloud services, deployment automation, platform operations, and data pipeline reliability.
• Hands-on experience with Snowflake constructs such as warehouses, stages, file formats, Snowpipe, Streams, Tasks, secure views, Time Travel, Zero-Copy Cloning, and Data Sharing.
• Experience with ETL/ELT design, incremental processing, data modelling, transformation logic, and production data pipeline operations.
• Working knowledge of at least one hyperscaler ecosystem: AWS, Azure, or GCP, including object storage, identity/access concepts, networking basics, and monitoring patterns.
• Experience with Python and scripting for automation, data processing, validation, or operational utilities.
• Familiarity with Git, CI/CD practices, release promotion, code review, and environment management.
• Understanding of data quality, reconciliation, lineage, metadata, access control, and security best practices.
• Experience working in Agile/Scrum or iterative delivery environments.

Preferred Qualifications:

• SnowPro Core or advanced Snowflake certification.
• Experience with dbt, Airflow, Azure Data Factory, Matillion, Fivetran, Informatica, Talend, or equivalent integration/orchestration tools.
• Exposure to Snowpark, Snowflake Cortex, vector search, semantic models, or AI/ML workloads on Snowflake.
• Experience with Databricks, Spark, Kafka, data lake, or lakehouse patterns.
• Understanding of Infrastructure as Code using Terraform, CloudFormation, ARM/Bicep, or similar tools.
• Experience in regulated, enterprise, or large-scale data environments with governance, auditability, and operational controls.

Success Measures:

• Reliable, tested, and supportable Snowflake pipelines delivered across environments.
• Improved data freshness, quality, observability, and production stability.
• Measurable improvements in query performance, workload efficiency, and Snowflake credit usage.
• Reusable engineering patterns that improve delivery consistency across Snowflake initiatives.

Soft Skills:

• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.
• Ability to work independently and within cross-functional teams.
• Strong attention to detail and commitment to quality.
• Eagerness to learn and adapt to evolving technologies.