Our ‘black belt’ specialists are leaders in their domains: digital champions, delivery-focused experts, top-tier security professionals, AI thought leaders, and engineering best practice advocates.
With a global footprint and deep local insight, Software at Scale delivers cutting-edge technology solutions that power mission-critical platforms. We solve complex engineering challenges at scale, driving quality, performance, and resilience through the strength of our people.
Empower Your Career
We’re seeking a highly proactive Senior Data Engineer to play a key role in delivering high-quality, scalable data platforms across our most important programmes. This mandate focuses on designing, building, and optimising modern data pipelines, transformation frameworks, and governed data products using Snowflake, dbt, and Airflow within a complex enterprise environment.
This role is hands-on and delivery-oriented. We are looking for an engineer who doesn’t just execute requirements but actively thinks outside the box to solve complex data engineering problems in creative ways. You will have the scope to heavily influence data architecture, establish performance and cost baselines, and drive technical excellence within our engineering teams.
Please note: Commercial experience with Snowflake is an absolute must-have for this role.
Key Responsibilities
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Proactive Problem Solving: Act as a technical catalyst within the data team. Autonomously identify pipeline bottlenecks, data quality issues, or process inefficiencies, thinking outside the box to engineer creative, unconventional solutions to complex enterprise data challenges.
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Data Pipeline Architecture: Design, build, and optimise scalable data pipelines and ELT workflows across Snowflake-based data platforms, working with large, complex, and semi-structured datasets.
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dbt Transformation Mastery: Develop robust ELT transformation frameworks using dbt (staging, intermediate, and mart layers). Build reusable models, macros, tests, documentation, and lineage to improve maintainability and transparency.
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Airflow Orchestration: Use Airflow to orchestrate data workflows, manage complex dependencies, schedule pipelines, and ensure production reliability.
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Snowflake Optimisation: Design and implement Snowflake data models, schemas, warehouses, tasks, and streams. Heavily optimise Snowflake workloads for performance, reliability, and cost efficiency.
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Data Quality & Governance: Implement rigorous data quality controls, validation checks, reconciliation logic, and freshness checks. Contribute to data governance, lineage, observability, and operational support practices.
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CI/CD & DevOps: Build and maintain CI/CD practices for data pipelines and transformation logic using Git-based delivery.
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AI Tooling Adoption: Champion the adoption of GenAI and agentic tools where they tangibly improve data engineering productivity, code quality, or system capability.
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Technical Mentorship & Collaboration: Translate business requirements into well-designed data solutions. Work closely with architecture, analytics, and business stakeholders, producing clear technical documentation (designs, data mappings, runbooks).
What You Bring
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Snowflake Mastery (Strictly Mandatory): Commercial experience with Snowflake is a must-have. You bring deep, hands-on expertise using Snowflake in a production environment, including performance tuning and cost optimisation.
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Creative & Proactive Mindset: You are a self-starter who cares about data quality, cost, and maintainability—not just getting data from point A to point B. You excel at thinking outside the box and applying creative problem-solving skills to overcome complex enterprise constraints.
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Senior-Level Expertise: Proven experience operating at a Senior Data Engineer or Analytics Engineer level within modern, high-performing engineering teams.
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dbt & Airflow Excellence: Mandatory, strong commercial experience building and maintaining dbt transformation layers and using Airflow for workflow orchestration and scheduling.
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SQL & Python Fluency: Advanced SQL skills (complex transformations, analytical queries) and strong Python experience for data engineering, automation, and pipeline development.
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Data Architecture: Strong understanding of data warehousing, dimensional modelling, and Medallion-style data architecture across batch and near-real-time workloads.
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Bonus Skills: Experience in banking, financial services, or regulated environments. Exposure to AWS (S3, Glue, Athena), data contracts, or holding a SnowPro certification is highly regarded.
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Communication: Strong communication skills and the ability to explain complex technical data concepts clearly to diverse stakeholders.
What We Offer
- Meaningful, hands-on data engineering work across complex, enterprise-grade platforms.
- Clear growth pathways from Senior to Staff and beyond.
- A low-ceremony, delivery-focused environment that values engineers who proactively own outcomes.
- Competitive remuneration and benefits.
- The opportunity to work alongside highly experienced engineers on challenging, high-impact systems.
