Analytics Engineer / Data Scientist (Mid–Senior)

Who we are

Venatus is an award-winning, global ad-tech platform that connects advertisers to the exclusive audiences of 500+ world renowned gaming and entertainment publishers. We are tech-first. We are gamers. We are the difference makers. Because when it comes to helping the world’s most recognisable brands such as EA, Nintendo and Rovio produce outstanding advertising campaigns, our in-house creative team alongside our direct and programmatic ad-sales have an unrivaled track-record. Our game-changing investment from leading private equity firm, LivingBridge makes this an exceptionally exciting time to join the company. Venatus has ambitious growth and expansion plans, launching new products and opening even more international offices. London is our HQ with regional offices in Amsterdam, New York, LA, Toronto, Sydney, Seoul and Manila.

Why you should work with us

Venatus is an extremely ambitious company and we pride ourselves on our open and supportive culture. Our talented employees stay loyal to our business due to the regular learning opportunities and experience of working directly with industry experts. We empower people to succeed, welcoming innovative ideas and ways of working that will ultimately help the business grow. We offer a fluid approach to hybrid working, giving employees the freedom to produce their best work, wherever they are. Joining Venatus is a play for the top. You’ll be challenged in positive ways, learn rapidly and develop a career in one of the most captivating industries in the world. You will be able to develop both professionally and as an individual, carving a career path that engages and excites you.

What we’re looking for

At Venatus, we pride ourselves on having the most detailed and robust data available in the industry for every campaign type - whether it be a programmatic auction or a direct sale. This role owns the step-change from “data exists” to “data is consumable”: trusted, well-modelled, well-documented, and easily usable across the business. You will do this through creating dashboards, self-serve semantic metrics, and next-gen agentic experiences. You will sit at the intersection of Data Engineering, Analytics, and the business, shaping how the company interacts with data day-to-day.

What you’ll own

• Build the company’s self-service data product
• Design and deliver clean, scalable, well-documented datasets (marts / data products) that become the default “source of truth”.
• Define a practical consumption strategy: what belongs in dashboards, what belongs in curated datasets, and what belongs in self-serve exploration and agentic workflows.
• Tooling and architecture leadership
• Lead the evaluation and selection of tooling for modelling, semantic metrics, catalog/dictionary, and consumption (dashboarding and/or agentic interfaces).
• Partner with Data Engineering to align on reference architecture, layering, SLAs, environments, CI/CD, and cost/performance trade-offs.
• Own the semantic layer & metrics governance
• Build and maintain the semantic layer (canonical metrics, dimensions, metric definitions, and reusable logic).
• Create and curate the data dictionary / business glossary: naming conventions, definitions, lineage, owners, and change management.
• Establish “definition of done” standards: tests, documentation, review, and release discipline.
• Own the verification and validation of all data outputs.
• Make data ML-ready
• Shape datasets for machine learning consumption: feature-ready tables, consistent entity definitions, time semantics, training/serving considerations, and backfill strategies.
• Collaborate with Data Science / ML engineering to ensure models are supported by stable, observable upstream data products.
• Enable streaming and near-real-time use cases
• Work with Data Engineering to enable streaming/near-real-time datasets where they materially improve decisions (e.g., operational monitoring, pacing, anomaly detection).
• Define modelling patterns for incremental/streaming data (late arrivals, dedupe, idempotency, watermarking, and quality checks).
• Drive adoption through enablement
• Train the business in how to use the semantic layer, self-serve datasets, and tooling: onboarding, documentation, office hours, and internal playbooks.
• Become the “bridge”: convert stakeholder questions into measurable metrics and scalable data products.

What success looks like:

• In 30–45 days: You’ve mapped the top consumption journeys (exec reporting, revenue, publisher, finance) and identified the top “trust blockers”. You’ve proposed a target architecture for modelling + semantic layer + dictionary (and the migration path).
• In 60–90 days: First golden data products shipped with tests + documentation + owners. Semantic layer MVP live for a core domain, with visible adoption (usage + reduced ad hoc).
• In 6 months: Self-serve has meaningfully reduced manual reporting burden; key metrics are consistently defined. ML teams have stable, feature-ready datasets with clear lineage and SLAs. Streaming/near-real-time datasets exist where they matter, with observability and quality gates.

What we’re looking for:

• Significant experience in analytics engineering / data modelling / BI engineering roles, delivering self-serve analytics at scale.
• Expert SQL and strong data modelling fundamentals (dimensional modelling, incremental patterns, performance tuning).
• Working knowledge of Python and experience with notebooks.
• Hands-on experience with a modern modelling workflow (e.g., dbt or equivalent) and strong Git + review discipline.
• Practical experience establishing semantic metrics, definitions, and governance that stick (not a theoretical catalog nobody uses).
• Strong stakeholder capability: you can translate ambiguous business needs into clear, measurable, testable data products.
• You can operate as a technical lead: set standards, unblock others, and drive decisions.

Nice-to-haves:

• Familiarity with ad-tech / retail media concepts: impressions, fill, pacing, yield, attribution/measurement, identity/privacy constraints.
• Experience with data observability, FinOps/cost governance, and production-grade data quality practices.
• Experience building data products for activation/audiences/measurement use cases or privacy-aware data collaboration patterns.

Typical tools you’ll use:

• Warehouses/lakes: Clickhouse
• Transformation: dbt
• Orchestration: Dagster

Package Private Healthcare

Salary Sacrifice

Flexible working pattern

Cycle to Work scheme

Eye Care Benefits – Free eye tests and up to £150 contribution toward glasses.

Summer Hours – Finish early at 3pm during the summer months!

25 days annual leave (inclusive of festive office closure) plus bank holidays and your birthday off

Diversity, Equity and Inclusion

We understand that the best ideas are born from the collaboration of diverse minds, spanning all races, religions, ethnicities, genders and orientations. We are dedicated to making Venatus a safe, happy place to be, allowing everyone to feel comfortable and confident in order to produce their best work. We employ a range of talent that represents the diverse creativity of our industry and we are proud of our growing teams of employees who share these values. If you have a disability or special need that requires accommodation during the application process, please let us know by emailing careers@venatus.com