At a Glance
- Tasks: Own and scale the data platform for impactful reporting and AI workflows.
- Company: Join a leading prop trading firm with a remote-first, collaborative culture.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and clear communication.
- Why this job: Make a real impact by transforming raw data into actionable insights.
- Qualifications: 3+ years in data engineering, strong SQL skills, and experience with data pipelines.
The predicted salary is between 63000 - 77000 £ per year.
Compensation $3,500-$5,500 / month gross. Prop Firm Match Global FZCO is a leading platform for discovering, comparing, and selecting top proprietary trading firms. We provide traders with tools and features to easily compare challenge details, read verified reviews, see accurate payout data, and much more. We're a fast-moving, fully remote team with members from all around the world caring deeply about the quality of what we build. Our culture values ownership, clear communication, and practical impact over fluff. Whether you're a trader, technologist, marketer, or operator - your work here shapes how thousands of users find and trust prop firms.
About The Department: Data & Analytics is the function that turns raw business data into decisions. We own the data platform, product analytics, marketing attribution, and revenue operations analytics. Our current priority is building reliable data infrastructure that supports both day-to-day reporting and the next wave of AI-driven workflows at PFM.
About The Role: Own and scale the core data platform that powers reporting, attribution, product analytics, reconciliation, AI workflows, and company-wide decision-making at PFM. Turn fragmented raw data into reliable, reusable business intelligence layers that enable faster decisions and scalable automation.
Performance Objectives
- Objective 1 - Own the data platform foundation
Outcome: Reliable, well-modelled, documented warehouse data that powers reporting and AI workflows across the company.
Typical tasks:- Own BigQuery warehouse architecture and dbt models
- Manage Airbyte pipelines and tracking infrastructure
- Apply orchestration tools (e.g., Airflow) for recurring workflows
- Monitor pipeline reliability and address breakages
- Objective 2 - Build reliable business data models
Outcome: Trusted source of truth across revenue, attribution, product funnels, partner performance, finance, and executive reporting.
Typical tasks:- Model revenue and commission flows end-to-end
- Model product funnels for conversion analysis
- Model partner performance for the Revenue Operations team
- Build executive reporting layers
- Objective 3 - Improve data quality and reduce manual work
Outcome: Reduced reconciliation discrepancies; reduced manual reporting overhead across Finance, Analytics, and Partners teams.
Typical tasks:- Implement governance, permissions, monitoring, and alerting
- Automate recurring reports and reconciliations
- Enable self-service analytics for non-technical stakeholders
- Improve documentation and discoverability of warehouse tables
- Objective 4 - Enable AI workflows and new integrations
Outcome: AI/automation initiatives scale on clean data; new sources are integrated without manual stitching.
Typical tasks:- Integrate QuickBooks, CRM, support, social, and operational systems
- Support AI enablement (Claude / internal AI assistants) with structured data
- Use AI-assisted coding workflows to speed development
- Partner with Engineering on integration architecture
Reporting cadence
- Reports to: Head of Data & Analytics
- Direct reports: None
- Key cross-functional partners: Product, Marketing, Finance, Operations, Analytics, Engineering
- Upward reporting cadence: Weekly 1:1 with Head of D&A; monthly written summary; quarterly OKR review
Location & work setup
- Remote, with strong overlap with CET hours (4+ hours of overlap)
- Working hours: roughly 9 AM - 6 PM CET, with reasonable flexibility
- Employment type: Full-time
Requirements
Must have:
- 3+ years of experience as a Data Engineer or Analytics Engineer
- Strong SQL and warehouse modelling skills (dbt, BigQuery preferred)
- Data pipeline engineering experience (Airbyte, orchestration, monitoring)
- Comfortable with workflow orchestration tools (Airflow or similar)
- Strong data governance, quality, and documentation mindset
- Excellent async written communication - we are remote-first
Nice to have:
- AI-assisted coding fluency (e.g., Claude, Cursor, Copilot)
- Prior experience in fintech, prop trading, or SaaS
- Experience integrating QuickBooks, CRM, or operational systems
- Cross-functional partnership experience with Product, Marketing, or Finance
Benefits
Hiring stages
We keep our process simple, transparent, and respectful of your time. Application screening: We carefully review all applications to identify candidates whose background aligns with the role's requirements. You'll receive a timely and transparent update on your application status. Video intro + role fit questionnaire: A short async step - no scheduling required. You'll record a brief video introduction and complete a written questionnaire. This helps us understand who you are, your relevant background, and how your experience aligns with the role without either side committing to a live call upfront. Discovery HR Interview: A conversation with our HR team focused on company and culture match. We'll walk you through how PFM works, what remote ownership looks like in practice, and what success in this role looks like. You'll have plenty of space to ask questions and share your goals. Real-world task: A short, scoped exercise based on real PFM data challenges - not abstract problems. This stage is designed to give both sides a realistic view of the work before committing further. A deep-dive Professional interview: You will meet the Head of Data & Analytics at PFM to focus on your technical expertise, analytical methodology, and how you approach real business problems. This is the decisive conversation on domain skills and role-specific experience. Functional Interview: A broader professional conversation with a senior member of the team: We'll explore your analytical thinking, how you communicate insights to different audiences, how you handle ambiguity, and how you operate within cross-functional environments. Peer & values conversation: A conversation with a peer from across the team focused on values, culture, and how you work best. You'll have plenty of space to ask questions and get a real feel for life at PFM.
If there's a strong mutual match, we start with an informal conversation to align on expectations, then follow up with a formal written offer. Full transparency throughout, and time for you to make an informed decision. To keep things fully transparent and mutually fair, we complete a reference check before the formal offer is extended. No surprises - just part of how we make careful, informed decisions on both sides.
Before applying take into account that: If you don't meet every single qualification but believe you can excel in the role based on what it requires - we encourage you to apply. We are an equal opportunity employer and welcome applicants from all backgrounds, experiences, and perspectives. Even if it's not listed as a formal requirement, we truly value candidates who have clear familiarity with the prop trading industry and us, our mission and what we do.
Not the right fit for this role? If this role isn't the right match but you have experience in trading operations, customer success, compliance, support, partnerships, or a related area - we still want to hear from you. Join our Talent Pool and we'll reach out when a relevant role opens. Every submission is reviewed, and the pool is always the first place we look before opening a new search.
Analytics / Data Engineer employer: Prop Firm Match
At Prop Firm Match Global FZCO, we pride ourselves on being a fully remote team that values ownership, clear communication, and impactful work. Our culture fosters continuous learning and collaboration, providing employees with ample opportunities for professional growth while working on innovative data solutions that shape the future of proprietary trading. With competitive compensation and a commitment to transparency in our hiring process, we ensure that every team member feels valued and empowered to make a difference.
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We think this is how you could land Analytics / Data Engineer
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We think you need these skills to ace Analytics / Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
