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 worlds 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. Youll 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 were 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 youll own

1. Build the companys 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.

1. 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.

1. 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.

1. 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.

1. 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).

1. 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 3045 days:

• Youve mapped the top consumption journeys (exec reporting revenue publisher finance) and identified the top trust blockers.

• Youve proposed a target architecture for modelling semantic layer dictionary (and the migration path).

In 6090 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 were 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 youll use:

• Warehouses/lakes: Clickhouse

• Transformation: dbt

• Orchestration: Dagster

Package

• Private Healthcare

• Salary Sacrifice

• Flexible working pattern

• Cycle to Work scheme

• Season Ticket Loan 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

Required Experience:

Senior IC