Senior Data Engineer
About this role
Soho, London Hybrid working: 3 days in the office Tues - Thurs The Role We are seeking a Senior Data Engineer to join our Data Team, taking a leading role in building the complex, scalable systems that power our data strategy. You will lead architectural discussions, own the design of business-critical data solutions and deliver high-quality systems that bring millions of fans closer to the sports they love. This is a fantastic opportunity to lead the optimisation of our data platforms for real-time AI/ML workloads, mentor engineers and make a significant impact on our business. The Senior Data Engineer leads technical initiatives, maintains data integrity and performance, and works across teams to translate complex business requirements into effective data solutions. The role also uses au
About data / ml roles
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
Typical skills: SQL, Python, pandas, basic stats; ML roles add scikit-learn / PyTorch / TensorFlow; engineering roles add Airflow / dbt / Spark
Salary insights (US, rough)
Typical range for data / ml roles in the US is $85,000–$240,000/year, varying widely with seniority, company stage, and city.
Estimates only. For company-specific numbers, check levels.fyi (tech), Glassdoor, or ask in the interview.
How to prep for the interview
Data interviews split: analysts get SQL + case-study questions, engineers get pipeline-design + coding, scientists get stats + ML modeling + sometimes a take-home dataset. Read the JD carefully and prepare to the actual flavor — don't waste time on PyTorch for an analyst role.
Common questions across all three: "How would you measure success for [product feature]?", "Walk me through a project where the data told you something surprising", and SQL window functions (LAG, ROW_NUMBER, PARTITION BY — practice these specifically). For ML roles, expect a question about overfitting, bias-variance tradeoff, and how you'd debug a model that performs well in training and badly in production.
Where this role typically leads
Career paths in data/ML have gotten messier as the discipline has matured. The clearest progression: Junior Analyst → Senior Analyst → Analytics Manager on the analyst side, Junior Data Engineer → Senior → Staff on the engineering side, and Junior DS → Senior DS → Principal DS / ML Lead on the modeling side. Salary peaks are highest for ML engineers + research scientists at big-tech firms.
Cross-discipline moves are common: analysts who learn engineering often outearn engineers who didn't learn business context. Stay close to the business — the most valuable data people are the ones executives bring into strategy discussions, not the ones building dashboards alone.
Red flags to watch for
- "Data scientist" role that's really a SQL analyst. Read the responsibilities carefully — if it's all dashboards and ad-hoc queries, it's an analyst role with a science title.
- No mention of stakeholder collaboration. Data work in isolation is usually data work that gets ignored. The role needs to be embedded with a business team.
- "AI / ML" listed everywhere but no specifics. If they can't name the actual problems they want to solve with ML, the company is buzzword-shopping.
- Asking for "10+ years of Python" or impossible combinations. Either the JD was written by HR without input from a data team, or the company has unrealistic expectations.
Frequently asked questions
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What does a data / ml role typically involve?
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
What's the typical salary range for data / ml roles in the US?
Roughly $85,000–$240,000 USD/year, depending on seniority, location, and company stage. This is a wide range on purpose — verify against levels.fyi or Glassdoor for the specific company.
