Data Engineer

🌍 Why this role exists

Sortlist is an AI-powered marketplace connecting companies with top service providers in marketing, web & IT. We help thousands of businesses find the right partners faster.

At Sortlist, data should align people around decisions — not trigger debates about metrics
.

We’re entering a new chapter for data. We’re moving from dashboard-centric analytics to a model-driven foundation where metrics are consistent, trusted, and scalable across teams. This evolution is key to unlocking real self-service analytics and, increasingly,
AI-assisted workflows like text-to-SQL.

We’re hiring someone excited by this transition — someone who wants to help define what modern BI looks like in a growing scale-up.

You’ll join a small, experienced data team and work closely with Product, Finance, Growth, and Engineering to make data trusted, shared, and genuinely useful
.

• Design and evolve analytical data models that remain stable as the product and business evolve
• Challenge vague or poorly framed questions and turn them into clear, well-defined metrics
• Make the “right number” easy to find, understand, and trust — especially for:
  • Financial performance
  • Investment and prioritisation decisions
  • Product focus and trade-offs
• Build and document data models that scale beyond you
  , enabling reliable self-service and AI-assisted analytics
• Play a key role in modernising our analytics tooling
  :
  • Contribute to the transition toward more modern data consumption patterns
  • Help ensure AI-assisted workflows can safely rely on strong, well-defined data models
  • Participate in selecting, configuring, and improving the tools used by end users

    Dashboards may exist.

Models and decisions are not optional.

• You focus on durability and rigour
  : models are testable, documented, and resilient to change
• You adapt your approach to how decisions are actually made — not how you wish they were made
• You communicate clearly with both technical and non-technical stakeholders
• You treat stakeholders as partners: you co-design metrics
  , you don’t just “take requirements”

Strong opinions about data quality and modeling are not just welcome — they’re expected.

You don't need to have led a data transformation before. What matters is your ability to think critically about trade-offs, communicate across teams, and improve your judgment over time.

This role will stretch your ability to:

• Think in systems — seeing how data decisions ripple across Product, Finance, and Growth
• Balance rigour with pragmatism — knowing when perfect is the enemy of good
• Influence without authority — building trust and co-designing metrics with stakeholders
• Shape tooling strategy — contributing to what modern BI looks like, not just using it

If you're mid-level, you'll learn how to design durable data models and navigate ambiguous business problems. If you're senior, you'll sharpen your ability to drive consensus and make strategic trade-offs.

Today, our core stack includes:

• Warehouse
  :
  Big Query
• Modeling layer
  : dbt
• BI / internal tools
  :
  Tableau, Retool

This stack is intentionally evolving
.

We are actively transitioning toward more modern, model-driven analytics tooling
, with a strong focus on:

• Reusable, well-defined data models
• Scalable self-service analytics
• AI-assisted data exploration built on solid foundations

You will be a key contributor in shaping this transition — both technically and conceptually.

This role is open to mid-level profiles and senior candidates
, under freelance or employee contracts.

You’ll thrive here if you:

• Are comfortable with ambiguity and incomplete problems
• Enjoy connecting data to real business and financial outcomes
• Are proactive and don’t wait for perfect instructions
• Care more about adoption and impact than ownership or control
• Like working with many different people and perspectives

Must-haves:

• Strong data modeling and durable analytics systems
• Turns vague questions into trusted metrics
• Communicates insights clearly to all stakeholders
• Drives adoption across teams

Nice-to-haves:

• Business acumen, links metrics to outcomes
• Solid foundations in data systems and pipelines
• Exp…