Sr. Data/Machine Learning Engineer (Adtech)
Role details
Job location
Tech stack
Job description
Drive transformative innovation by building and scaling the data and AI feature infrastructure for AI-powered ranking, bidding and recommendation systems in digital advertising. Lead the design and implementation of high-performance data pipelines and feature stores that ensure data consistency, low latency, and reliability. As a technical leader, you will elevate our feature engineering and data delivery capabilities, enabling advanced modeling at scale and shaping the future of data-driven decision-making.
Requirements
Do you have experience in Spark?, * Exceptional Data Engineering & Infrastructure Mastery:
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Mastery in designing and building efficient data pipelines to feed AI ranking, bidding and recommendation systems.
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Expertise in implementing and maintaining Feature Stores (online and offline) for fast and consistent feature serving to ML models.
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Advanced knowledge of streaming data technologies (e.g., Kafka, Pub/Sub) and large-scale data processing engines (e.g., Spark, Flink) in a cloud environment (GCP/AWS).
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Advertising Data Domain Knowledge:
- In-depth understanding of AdTech data sources (eg bid request logs, server logs, front-end events) and the infrastructure required to process them.
- Proven experience building data flows that support the feature engineering necessary to optimise CPC, CPA, and ROAS metrics for advertisers.
- Exceptional Data Processing and System Expertise:
- Advanced proficiency in Python (for building robust ETL/ELT) and SQL (for data modeling and warehousing).
- Strong foundation in data modeling, schema design, and ensuring data quality/integrity for high-volume ML features.
- Ability to design reliable, scalable systems that support complex feature transformations required for multi-objective optimisation models.
- Proven Leadership in Data Systems:
- A track record of designing and deploying high-performance data pipelines and feature infrastructure in production, with measurable stability and performance.
- Experience influencing data architecture strategy and driving adoption of best practices for data consumption by Data Science teams.
- A history of mentoring junior engineers and driving alignment on data infrastructure standards across engineering and data organizations., Nice-to-Haves:
- Deep MLOps and Infrastructure Experience: Expertise in managing and optimising feature serving infrastructure for low-latency requirements, including techniques like caching, sharding, and geographically distributed serving for high-volume prediction and decision services.
- Feature Layering for Causal/Economic Models: Familiarity with the data requirements and pipeline design to support features for econometric models or causal inference (eg handling complex time-series lags, external shock data).
- Cloud-Native Data Ecosystems: Certified expertise (or equivalent experience) in specific cloud-native data services (eg Google Cloud Dataflow, BigQuery, Vertex AI Feature Store, AWS Kinesis) that enable full operationalisation of the ML feature lifecycle.
- Advanced ML Architecture Knowledge: Working knowledge of advanced ML architectures, such as Transformer-based models or Graph Neural Networks, to anticipate and design the necessary feature representations and data flows required by Data Scientists.
