Data Engineer

• Build and maintain ingestion pipelines (REST APIs, SFTP, database replication) into Snowflake
• Write dbt models across bronze/silver/gold layers following established conventions
• Integrate with third-party ESPs (DotDigital, Adestra, FastTrack): push segments, pull back campaign events (opens, clicks, bounces, conversions)
• Develop and maintain AWS Lambda functions for data extraction and reverse ETL
• Build data models for identity resolution, segmentation, and attribution
• Configure and manage Airflow DAGs for orchestration
• Implement data quality checks, monitoring, and alerting
• Manage Snowflake and AWS infrastructure via Terraform
• Investigate and resolve data incidents (pipeline failures, data quality issues, PII exposure)

• Actively design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting Insights and Finance use cases that analyze technology usage and costs (e.g. AWS CUR).
• Design and implement complex data models using industry best practices that capture a complete view of technology usage and costs, while ensuring accuracy, scalability, and long-term usability.
• Partner closely with internal stakeholders to understand their needs and craft solutions that drive their ultimate outcomes.
• Architect and implement robust, maintainable, and high-performance data solutions.
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for finance processes.
• Mentor junior data engineers through code reviews and technical guidance, setting best practices and fostering technical growth.
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility.
• Ensure data quality and governance.

Staff Data Engineer – Technology Strategy and Ops, Gaming Industry

• Deliver Flex-critical data needs: build and maintain reliable pipelines and datasets that enable Flex models (e.g., demand/availability signals, aggregations, monitoring).
• Evolve the data platform: assess what we have today and drive pragmatic improvements in architecture, tooling, and operating practices—setting the direction for how the platform matures.
• Own data quality and trust: implement testing, lineage/definitions, and guardrails (e.g., dbt tests, anomaly detection, freshness checks) so stakeholders can trust outputs.
• Enable self-serve analytics: produce well modelled datasets and documentation that make it easy for others to answer questions without bespoke work.
• Partner on data science work: collaborate on data readiness for modelling, feature pipelines, evaluation workflows, and productionisation concerns (even if you're not the primary model builder).
• Make high-leverage tech choices: propose and justify changes (or non-changes) to tools and processes, bringing the experience to know when to build, when to buy, and when to leave well alone.

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Data Engineer

Staff Data Engineer – Technology Strategy and Ops, Gaming Industry

Senior Data Engineer