Engineering Lead - Snowflake -Data Engineering
Role details
Job location
Tech stack
Job description
Within their area, the Engineering Lead will focus on the continued improvement of data design, implementation, and delivery. This will be achieved by ensuring that sustainable engineering practices are prioritised and embedded in the way teams work. As an expert in their data development field, the Engineering Lead will guide and mentor developers (including 3rd party partners), setting technical objectives and reviewing subsequent performance.
The Engineering Lead's responsibilities and objectives are broadly split into two areas:
- Snowflake-led data engineering excellence
Within their area, the Engineering Lead will focus on the continued improvement of data design, implementation and delivery on Snowflake. This will be achieved by ensuring that sustainable engineering practices are prioritised and embedded in the way teams work.
As the technical authority for Snowflake within their domain, the Engineering Lead will:
- Own and evolve Snowflake architectural patterns, standards and best practices
- Ensure data ingestion, transformation and modelling approaches are optimised for Snowflake
- Drive performance, cost optimisation and scalability within Snowflake workloads
- Embed strong governance, security and data quality practices aligned to Snowflake capabilities
- Guide and mentor developers (including 3rd-party partners), setting technical objectives and reviewing subsequent performance
- Enterprise collaboration & platform evolution
The Engineering Lead will work collaboratively with both Enterprise Engineering and Technology to help shape the development culture at Schroders, contributing to:
- Standards, patterns and reference architectures for Snowflake-based data solutions
- Shared components and reusable Snowflake assetsImprovements in how we deliver data engineering and software to the business, The Engineering Lead will work collaboratively with both Enterprise Engineering and Enterprise Software Engineering to help shape the development culture at Schroders, contributing to standards, patterns, practices, reference architectures, shared components, and other things that will improve the way we deliver engineering and software to the business.
The Data Engineering team owns:
- Snowflake-based data engineering for acquisition and integration of enterprise data assets
- The foundational data layers that underpin analytics and downstream data services
Engineering Leads also form part of the broader Enterprise Engineering function within Enterprise Technology, helping Schroders deliver predictable, high-quality and supportable software and data platforms.
What you'll do
- Technically lead the delivery of Snowflake-based data sources, pipelines and data products
- Partner closely with business subject-matter experts and technology counterparts to deliver Snowflake-centric data solutions
- Lead the creation of reusable Snowflake patterns, templates and standards
- Act as a people manager and mentor, supporting the growth of your engineers
- Contribute to analysis, solution design, implementation and testing of Snowflake data pipelines
- Work within backlog-focused squads, contributing across the full delivery lifecycleOperate comfortably within agile delivery methodologies (Scrum or Kanban)
Requirements
Do you have experience in Scrum?, The Engineering Lead is a highly proficient, versatile and hands-on engineer with excellent communication skills. The Engineering Lead works across one or more engineering delivery teams to deliver high-quality data, quickly and reliably, with Snowflake as the core enterprise data platform.
The role requires a high level of competence and up-to-date knowledge of modern data engineering practices, cloud-native data platforms, and Snowflake design patterns. The Engineering Lead will also strive to create a collaborative engineering culture., * Strong, hands-on Snowflake experience in an enterprise environment
- Excellent SQL / Snowflake SQL knowledge, including Query optimisation and performance profiling. Understanding of different SQL engines and optimisation trade-offs
- Strong Python skills within data engineering
- Experience building robust, failure-tolerant data pipelines on Snowflake
- Practical understanding of ELT patterns, idempotency and data engineering best practices
- Experience implementing data quality frameworks
- Demonstrable competency in data modelling.
- Experience with cloud platforms, ideally Azure and/or AWS
- Strong knowledge of GitHub and shared codebasesGood working knowledge of agile delivery practices
The knowledge, experience and qualifications that will help
- Snowflake certifications
- Experience with Airflow, dbt, Docker and/or Kubernetes
- Experience implementing or supporting Data Mesh-style approachesAI / ML or automation experience leveraging Snowflake data
What you'll be like
- Friendly, approachable and collaborative, with a mentoring mindset
- Comfortable acting as a Snowflake technical authority
- Self-motivated with a continuous improvement mindset
- A pragmatic problem solver, comfortable with ambiguityDown-to-earth, honest and able to communicate opposing ideas constructively
