Senior Data Engineer - ML Analytics

Who is Lift? lift® is the only audience management platform built specifically for the iGaming industry. Designed to help operators capture, segment and activate their first-party data, Lift enables smarter, data-driven campaign decisions that maximise ROI across programmatic, search and social media channels. With advanced machine learning and a deep understanding of player behaviour, we empower brands to deliver full-funnel marketing strategies for acquisition and retention - globally. The role We are searching for a Senior Data Engineer to join Lift’s data engineering team and help extend our data platform into advanced analytics and machine learning. Our team is already building the core data pipelines and reporting infrastructure. This role will focus on developing the next layer of data intelligence, including audience modelling, behavioural analysis, and predictive systems. The role focuses on transforming high volume AdTech and iGaming event data into structured intelligence that helps understand user behaviour, intent, and value across acquisition and retention journeys. This includes designing and optimising Snowflake and AWS pipelines, integrating complex first- and third-party data sources, and building scalable datasets that support advanced analytics and machine learning. The role will also involve developing analytical and machine learning systems that identify behavioural patterns within bidstream and conversion data, enabling audience intelligence, anomaly detection, performance benchmarking, and predictive optimisation. Working closely with Lift’s engineering team, this role will help evolve the platform’s capabilities from reporting and analytics into audience intelligence, predictive modelling, and AI-assisted insight generation. Outputs from these systems will feed directly into the Lift platform to support audience targeting, campaign optimisation, and automated client insights. Key Responsibilities Data Engineering Work with the data engineering team to design and maintain scalable data pipelines processing large volumes of event-level data from internal tracking systems and external DSP integrations. Contribute to the development and optimisation of data models within Snowflake to support analytics, reporting, and machine learning workloads. Integrate multiple first- and third-party data sources into a scalable, cost-efficient data platform. Ensure data quality, performance, and reliability across Lift’s analytics infrastructure. Identity & Audience Intelligence Develop systems to resolve user identities across multiple identifiers including cookies, fingerprints, and declared user IDs. Build audience datasets and segmentation pipelines used for targeting and activation. Develop datasets that power user recency, engagement analysis, and value modelling. Extract behavioural signals from bidstream and tracking data to understand user intent and lifecycle patterns. Machine Learning & Data Science Develop and experiment with predictive models including: Lifetime value prediction Deposit propensity modelling User value segmentation Campaign performance forecasting Creative fatigue detection Work with engineering teams to deploy machine learning models into production workflows. Continuously evaluate and improve model performance using real campaign data. Analytics & Data Products Support the development of advanced analytics capabilities within the LiftDSP platform. Transform raw event-level data into structured datasets that power reporting, optimisation tools, and product features. Contribute to the development of AI-driven semantic insight systems that translate complex analytics outputs into clear, actionable client intelligence. Collaborate with engineering and product teams to build data-driven capabilities into the platform. Technical Requirements Strong SQL and experience working with analytical warehouses such as Snowflake . Strong Python experience for data engineering, analytics, and machine learning. Experience building and maintaining data pipelines in AWS environments . Experience working with large scale behavioural or event level datasets. Understanding of machine learning fundamentals and model evaluation. Experience with machine learning libraries such as scikit-learn, PyTorch, or TensorFlow. Nice to Have Experience working with advertising, marketing, or behavioural datasets. Experience building identity resolution or user-level datasets. Experience deploying machine learning models into production environments. Experience with AWS services such as S3, Lambda, Glue, or EC2. What Success Looks Like Within the first 6–12 months the successful candidate will have: Built scalable data pipelines and datasets powering analytics and audience intelligence Developed user-level feature datasets combining multiple identity signals Delivered initial predictive models supporting campaign optimisation and audience segmentation Helped evolve LiftDSP’s platform from reporting into data-driven audience intelligence and predictive analytics What are we offering? This role offers an exciting opportunity to join a high-paced, fast-growth business backed by a successful investor. You will work closely with an experienced leadership team in a senior, influential position, with the opportunity to shape how the company handles and utilises its data. The role also benefits from a flexible working culture, and direct exposure to senior leadership and strategic decision-making. We have a great team, a friendly, welcoming environment, and a very positive can-do culture.