Data Analytics Engineer/Permanent/London Hybrid

• Role Data Analytics Engineer
• Location: London. Hybrid model - 3 days/week
• Skills: Data modelling + engineering, SQL, DBM, Data vault, Snowflake.

As a Data Analytics Engineer

Key responsibilities:

• ETL Pipeline Development: Develop, optimise, and maintain ETL pipelines to efficiently extract, transform, and load data from various sources, ensuring high data quality.
• Datamodelling & Pipeline Design: Design, develop, and maintain robust data models and pipelines to deliver high-quality data products that meet business needs.
• Query Optimization & Data Transformation: Write and optimize SQL queries, ensuring data integrity, performance and scalability, using best practices and techniques.
• Data vault Model implementation: Implement flexible Data vault model in Snowflake to support large-scale analytics and business intelligence.
• Cross-Team Collaboration: Collaborate with Data Engineers, Product Managers, and Data Scientists to deliver solutions that support data-driven insights and innovation.
• Stakeholder Engagement: Engage with business stakeholders to understand requirements and translate them into technical solutions that add value.
• Data Quality & Governance: Implement and enforce data governance and quality processes, ensuring accurate and consistent data flows across all systems.
• Cloud & Infrastructure Support: Work with cloud platforms such as AWS/Azure and DBT with Snowflake to build and maintain scalable data solutions.
• Continuous Improvement: Proactively look for ways to improve data systems, processes, and tools, ensuring efficiency and scalability.

Your Profile:

Key skills/knowledge/experience:

• ETL/ELT & Data Pipelines: Solid understanding of ETL/ELT processes, along with hands-on experience building and maintaining data pipelines using DBT, Snowflake, Python, Terraform and Airflow.
• Data Modeling Experience: Strong experience designing and implementing data models (Data Vault or fact and dimensional) for analytics and reporting purposes.
• Cloud Platforms: Experience working with cloud data warehouses and analytics platforms, such as Snowflake, AWS, Azure, and Databricks.
• GitHub Skills and Experience: Proficiency in using GitHub for version control, code collaboration and managing data engineering projects.
• Data Governance and Compliance: Expertise in implementing data governance frameworks in Alation, including data quality management and compliance with industry regulations.
• Effective Communication and Collaboration: Excellent communication skills for interacting with stakeholders, presenting technical concepts, and collaborating with cross-functional teams.
• Collaboration & Communication: Strong interpersonal skills with the ability to work cross-functionally with stakeholders, engineers, and analysts.
• SQL Proficiency: Expertise in writing complex SQL queries, query optimization, and database design for analytics.
• Problem-Solving & Analytical Thinking: Ability to think critically and solve complex problems, translating business requirements into actionable insights.

Good to have:

• Knowledge of Data Visualization Tools: Experience with tools such as MicroStrategy/Power BI.
• Terraform: Experience with Terraform and Terragrunt for infrastructure as code.
• GenAI: Experience with Generative AI technologies.