Responsibilities
- Delivering data work on client engagements: pipelines, transformation layers, models, and the infrastructure around them
- Adapting to different client contexts, legacy warehouse migrations, greenfield lakehouses, struggling transformation layers that need rescuing
- Defining and evolving our internal standards for data work: testing, documentation, project structure, code review
- Mentoring and growing data engineers across the company
- Showing up for client team moments (internal speakers, learning sessions, social events) to build strong working relationships.
- Contributing to proposals and helping shape engagements during the sales process
- Representing YLD externally through writing, speaking, and contributing to the wider data community.
Requirements
- SQL as engineering: You write SQL that's reviewable, testable, and performs at scale. You understand query planning, engine quirks, and how materialisation choices affect cost and performance
- Python for data: You write typed, testable Python for pipelines and tooling. You've built and maintained production data systems, not just notebooks
- Data modelling: You've made deliberate choices between dimensional, Data Vault, normalised, and denormalised designs. You can explain those trade-offs in terms of flexibility, query performance, and maintainability
- Transformation architecture: You design transformations that are idempotent, incremental, and dependency-aware. You think in DAGs, not scripts
- Pipeline design: You understand batch, streaming, and micro-batch trade-offs. You consider latency, complexity, cost, and reprocessability. And choose the right approach for the problem
- Data testing: You write tests as code, schema contracts, assertions, and transformation logic. You know what to catch at build time vs. defer to observability
- Data observability: You treat data reliability like site reliability. Measurable indicators, alerting, incident response, and root cause analysis
- CI/CD for data: You version, test, and deploy pipelines like software. Environment promotion, rollback strategies, and infrastructure as code for reproducible environments
- Data governance: You've implemented lineage, cataloguing, sensitive data classification, or access control. Not just built pipelines, but made them auditable and secure
- Cost-conscious: You've optimised warehouse spend, storage strategies, or job efficiency. You treat compute as a resource to manage, not ignore
- Platform fluency: You've worked across the modern stack. Orchestration (Airflow, Dagster), warehouses (Snowflake, BigQuery, Databricks), ingestion (Fivetran, Airbyte, custom), and transformation (dbt, Spark)
- Cross-functional fluency: You translate engineering constraints into business terms and negotiate realistic commitments
Benefits
- Company Private Health care
- Enhanced fully paid maternity and paternity leave for up to 6 months
- Company’s Pension Scheme (UK Only)
- 25 days annual holiday (excluding Public Holidays)
- £2000/€2000 annual allowance for Training/Conferences
- £300/€300 annual allowance for additional hardware
- Wellbeing & Performance Support via Oli including Therapy and Coaching
- Discretionary Bonus (depending on Company performance and results)
- Company laptop
- Generous referral schemes
Work Arrangement
Hybrid
Additional Information
- We live and breathe our values, and know you will too: Growing every day, Including everyone, Relationships built on honesty and ethics, Inspiring solutions, Winning together
- We’re an equal-opportunity employer and value diversity in all its forms. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, pregnancy or maternity, age, marital status, or disability. We also offer a remote-first working environment, with flexible working and work–life balance as standard for all employees.
