Data Engineer – Snowflake

Role Overview

We are looking for an experienced Data Engineer with strong hands-on expertise in Snowflake, SnapLogic, and dbt to design, develop, and maintain reliable, scalable, and cost-effective data solutions. The ideal candidate will have a proven track record of delivering production-grade data pipelines and modern ELT architectures, alongside a practical, responsible approach to using AI-assisted development tools.

In this role, you will collaborate with data quality, governance, analytics, and engineering teams to ensure data is accurate, well-modelled, secure, documented, and ready for business use. You will also help establish reusable AI-enabled engineering practices and support the development of junior team members.

Key Responsibilities

Data Engineering and Integration

• Design, develop, deploy, and maintain robust ETL/ELT pipelines that ingest and transform data for the Snowflake platform.
• Build and manage integrations using SnapLogic, connecting source systems, APIs, cloud services, and downstream consumers.
• Develop reliable, scalable data ingestion and transformation workflows, including appropriate error handling, monitoring, and recovery mechanisms.
• Work with orchestration tools such as Apache Airflow, Azure Data Factory (ADF), or equivalent platforms.

Data Modelling and Transformation

• Develop and maintain modular, reusable data transformations using dbt, including models, macros, dependencies, tests, and documentation.
• Design and implement dimensional data models and layered architectures, including Bronze/Silver/Gold or medallion-style patterns.
• Evaluate normalisation and denormalisation trade-offs to balance usability, maintainability, and query performance.
• Establish data quality checks and collaborate with governance and analytics teams to improve data consistency, lineage, and trustworthiness.

Snowflake Performance, Security, and Cost

• Design and maintain Snowflake data structures, schemas, warehouses, and access controls in line with platform standards.
• Monitor and optimise query performance, warehouse utilisation, and compute costs using appropriate techniques such as query tuning, clustering where beneficial, and resource monitors.
• Apply security and access-management best practices, including role-based access control and least-privilege principles.
• Troubleshoot production issues and implement improvements to pipeline reliability, performance, and operational visibility.

Engineering Practices and CI/CD

• Write clean, maintainable, tested, and version-controlled SQL, Python, and configuration code.
• Contribute to Git-based workflows and CI/CD pipelines for validating, testing, and deploying data integrations and dbt projects.
• Participate in code reviews and promote engineering standards, reusable components, and consistent development practices.
• Maintain technical documentation for pipelines, models, dependencies, operational procedures, and data quality expectations.

AI-Assisted Engineering

• Use AI coding assistants such as GitHub Copilot, Claude Code, Cursor, or ChatGPT to accelerate development, debugging, testing, and documentation.
• Apply AI tools to draft SQL and dbt models, generate test cases, explain pipeline failures, summarise data quality issues, and improve technical documentation.
• Review and validate AI-generated code and recommendations before use, with particular attention to correctness, security, performance, and data privacy.
• Contribute reusable prompts, workflows, and internal guidance that help engineering teams use AI tools safely and effectively.
• Explore opportunities to apply AI to data engineering challenges such as anomaly detection, metadata generation, pipeline troubleshooting, and code-review support.

Collaboration and Mentoring

• Work closely with data quality, governance, analytics, and platform teams to deliver trusted data products.
• Mentor junior engineers in data engineering practices, code quality, data modelling, and responsible use of AI-assisted tools.
• Share knowledge, contribute to technical discussions, and help improve team standards and delivery practices.

Required Skills and Qualifications

• Experience: 8+ years of experience in data engineering, including at least 4+ years of hands-on experience with Snowflake and modern ELT tooling such as dbt.
• Snowflake: Strong practical experience in data modelling, SQL development, performance tuning, security/access management, and cost optimisation.
• SnapLogic: Experience building, deploying, and maintaining integration pipelines across heterogeneous source systems and APIs.
• dbt: Experience developing modular transformations, managing model dependencies and macros, implementing tests, and maintaining documentation.
• Data Modelling: Strong understanding of dimensional modelling, layered/medallion architectures, and normalisation versus denormalisation trade-offs.
• SQL and Python: Advanced SQL skills and practical experience with Python or another scripting language.
• Orchestration: Familiarity with Apache Airflow, Azure Data Factory, or a comparable orchestration tool.
• Version Control and CI/CD: Working knowledge of Git-based development, automated testing, and deployment workflows.
• Production Support: Demonstrated ability to troubleshoot issues and deliver reliable, production-grade data pipelines at scale.
• AI-Assisted Development: Regular use of AI coding assistants, with the judgement to verify outputs rather than relying on them without review.
• Communication: Ability to collaborate across teams, explain technical decisions, and document solutions clearly.

Nice to Have

• Experience with data quality and governance tools such as SODA, Collibra, or similar platforms.
• Exposure to cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
• Experience working in regulated, security-conscious, or enterprise-scale data environments.
• Previous experience mentoring engineers or leading a small engineering pod.
• Experience with data observability, lineage, metadata management, or automated anomaly detection.

AI-Augmented Working Expectations

We value engineers who use AI as a practical part of their day-to-day workflow while applying sound engineering judgement. Successful candidates should be able to:

• Select appropriate AI tools for coding, testing, debugging, and documentation tasks.
• Write clear prompts and provide sufficient context while protecting confidential or sensitive data.
• Validate generated SQL, Python, dbt models, and configuration before merging or deploying changes.
• Recognise when AI-generated output may be incomplete, inaccurate, insecure, or inefficient.
• Share effective prompts and repeatable workflows that improve team productivity without compromising quality.

What Success Looks Like

• Reliable, maintainable, and well-monitored data pipelines that meet agreed data quality and availability expectations.
• Well-structured, documented, and tested dbt models that support analytics and reporting needs.
• Snowflake workloads that balance performance, scalability, security, and cost.
• Effective collaboration with data consumers and platform stakeholders.
• Responsible adoption of AI-assisted engineering practices that improve productivity and code quality.
• Knowledge sharing and stronger engineering capability across the team.

Experience Level

Senior individual contributor — 8+ years of overall data engineering experience, including 4+ years focused on Snowflake and modern ELT practices.

Availability: Immediate joiner.

Core Technology Stack

• Data Platform: Snowflake
• Integration: SnapLogic
• Transformation: dbt
• Languages: SQL, Python
• Orchestration: Apache Airflow, Azure Data Factory, or equivalent
• Engineering Workflow: Git, CI/CD
• AI Assistants: GitHub Copilot, Claude Code, Cursor, ChatGPT, or equivalent
• Cloud Platforms: Azure, AWS, or GCP