Senior Data Engineer and Analyst - Contract
Schedule: 3 days per week (flexible arrangement to be agreed).
Duration: 11 months (3-month rolling contract, reviewed at each milestone)
Rate: £450 per day (3-month rolling contract)
About Modern Milkman
Everyday life adds up. The grocery list on the fridge, the jobs you keep meaning to sort - Modern Milkman helps people stay on top of it all, right from their doorstep, while making the better choice the easier one: less waste, more homegrown producers, a new life for the things you no longer need.
What started as a single milkround in Lancashire has grown into a global platform, now helping hundreds of thousands of households across the UK and US run their homes in a better way.
Doing that well, at scale, takes brilliant people. Which is where you come in.
Modern Milkman is seeking an experienced Data Engineer/Analyst Hybrid to join our lean data team on a 3-day-per-week basis for 12 months (3-month rolling contract). This is a foundational role combining data infrastructure build, analytical insights delivery, and digital analytics ownership.
Our data team is at an inflection point. We have solid foundational tooling (Looker, BigQuery, dbt), but our core data structures, business rules, and digital data feeds are incomplete or inaccurate. This is the primary blocker to self-serve analytics and meaningful insights work across the business. We need someone who can engineer the solution, deliver the insights, and own the digital data layer simultaneously—fixing the foundation whilst unlocking value for stakeholders.
The Role
You'll act as a bridge between raw data and reliable, actionable insights, working across three integrated disciplines:
Data Engineering – Fix and improve our data feeds, validate business rules, build robust data models in our BI platform (Looker), and maintain observability of our data stack.
Analytics & Insights – Conduct analysis that informs both day-to-day decisions and business strategy; empower stakeholders with self-serve analytics and upskill them in data literacy.
Digital Analytics – Own the integrity of our digital data layer (tag management, event tracking, data collection); provide product and growth insights; support conversion rate optimisation and experimentation.
Key Responsibilities
Data Engineering & Infrastructure
Diagnose and fix inaccurate data feeds, incomplete data structures, and incorrect business rules in Looker and our underlying warehouse.
Design, build, and maintain data models (dbt) that serve both reporting and self-serve use cases.
Implement and improve ELT pipelines and data transformations, ensuring reliability and performance.
Develop and maintain data quality processes and monitoring (validation, observability, SLA enforcement).
Proactively identify data issues, root cause failures, and address them promptly.
Document data assets, models, pipelines, and transformations with clarity and accessibility in mind.
Optimise data stack performance and cost efficiency.
Analytics & Insights Delivery
Conduct ad hoc and exploratory analysis to answer key business questions and inform decision-making.
Create, maintain, and continuously improve self-serve data products (Looker dashboards, reports) that empower stakeholders to generate their own insights.
Translate business problems into analytical solutions; identify root causes and recommend actionable outcomes.
Communicate insights effectively to non-technical stakeholders using storytelling and clear business language.
Upskill stakeholders in data literacy, helping them understand and trust digital data and analytics outputs.
Work with stakeholders to scope data requirements and understand their challenges and needs.
Digital Analytics Ownership
Own the integrity of our digital data assets—tag management, event tracking, web/app data layer, and data collection strategy.
Drive the roadmap and strategy for digital data collection and management, ensuring high data integrity.
Provide actionable insights to growth and product teams on user behaviour, conversion optimisation, and product opportunities.
Support A/B testing design, execution, and analysis; contribute to broader conversion rate optimisation strategy.
Ensure digital metrics and KPIs are well-defined, understood, and accurately measured across the business.
Collaborate with tech and product teams to maintain the quality of the digital data layer.
Team Collaboration
Work closely with the analytics engineer on data engineering priorities, knowledge sharing, and continuous improvement.
Work with product team and peers to triage data requests and issues; deliver appropriate solutions and escalate where needed.
Build and maintain relationships with stakeholders across growth, product, tech, and operations teams.
Support the data team in shaping the data and analytics roadmap, balancing strategic and tactical priorities.
Ideal Candidate Profile
You thrive in foundational, fix-it-once environments where the work is varied and impact is immediate. You're equally comfortable writing SQL pipelines, crafting analytical narratives, and debugging tag management issues. You have:
Technical Skills
SQL expertise – You write clean, efficient queries and understand data modelling principles.
Data transformation & ELT experience – Proficiency with dbt, Fivetran, or similar tools.
Cloud data warehouse knowledge – Experience with BigQuery, Snowflake, or Databricks.
BI/analytics tools – Hands-on experience with Looker (or similar; you can learn Looker quickly).
Python or scripting capability – A plus, particularly for data quality workflows and automation.
Digital analytics fundamentals – Tag management, event tracking, data layers, conversion tracking. Experience with Google Analytics, tag managers (GTM), or similar.
Analytical & Problem-Solving
Strong ability to grasp business needs, identify root causes, and translate them into data solutions.
Proven ability to use statistical analysis, data mining, and critical thinking to deliver commercial value.
Comfortable with ambiguity; you diagnose problems, propose solutions, and move forward with limited direction.
A "solve it once" mentality—you build reliable, scalable solutions rather than quick fixes.
Stakeholder & Communication Skills
Excellent communication skills across technical and non-technical audiences.
Ability to translate complex data concepts into simple, actionable language.
Strong relationship-building—you cultivate trust with stakeholders by understanding their challenges and delivering appropriate solutions.
Proactive and driven; you take initiative, identify high-value work, and keep momentum in a lean environment.
Behaviours & Mindset
Commercially aware – You focus on business outcomes and value over project outputs.
Adaptable and flexible – You context-switch between engineering, analytics, and digital work seamlessly.
Detail-oriented yet strategic – You balance precision in technical work with clarity on business impact.
Curious and self-directed – You stay updated with data best practices, evaluate new tools thoughtfully, and continuously improve.
Resilient with a growth mindset – You're comfortable working in a lean team and obsessed with learning.
Data champion – You advocate for data quality, governance, and documentation as embedded practices.
What You'll Bring
Solid experience in a data-related role (analytics engineer, analytics, or data engineer), ideally in a fast-paced or early-stage environment.
Experience fixing broken or incomplete data landscapes – You've diagnosed data quality issues, rebuilt pipelines, and improved BI governance.
Proficiency with at least one BI tool (Looker preferred, but ThoughtSpot, Tableau, or similar transferable).
Familiarity with A/B testing and experimentation design – A plus.
Experience owning a data layer (digital, product, or operational) – A plus.
