Top Tech Jobs & Startup Jobs in London

Build and optimize scalable batch and streaming data pipelines for an AWS-based sportsbook lakehouse. Develop medallion architecture data assets, model sportsbook datasets, implement transformations, and improve data quality, observability, governance, metadata, and lineage. Design schemas and data contracts for distributed systems while supporting analytics, trading, risk, personalization, and machine learning use cases. Collaborate with data, analytics, and business stakeholders to deliver trusted, real-time data products.

Design and maintain scalable data platforms, lakehouse architectures, pipelines, and data products across cloud and on-premises environments. Build streaming, ETL/ELT, transformation, orchestration, CI/CD, governance, and self-service analytics solutions. Collaborate with stakeholders and architects to translate business needs into technical solutions, support data quality and compliance, and provide operational support. Explore generative AI tools such as Amazon Q and Copilot to improve analytics accessibility and efficiency.

Build and maintain batch and streaming data pipelines for a modern AWS sportsbook data platform. Develop medallion-aligned datasets using SQL, dbt, Spark, and related tools; support Kafka or Flink streaming; implement data quality checks, schemas, monitoring, and governance; and troubleshoot pipeline performance and costs. Collaborate with analytics, data science, machine learning, trading, risk, and personalization teams to deliver reliable, scalable data assets.

Build and optimize scalable ETL/ELT pipelines, Spark jobs, Airflow DAGs, and dbt transformations. Design Lakehouse architectures using S3, Iceberg, and Delta Lake; develop reusable platform frameworks, CI/CD pipelines, data contracts, observability, and data quality controls. The role also supports streaming technologies, cloud data warehouses, dimensional modeling, schema evolution, and AI-enabled engineering improvements.

Build, optimize, and deploy production-grade low-latency pricing services and microservices for sportsbook quantitative models. Develop robust APIs and streaming interfaces, improve technical requirements, champion testing, observability, CI/CD, and automation practices, review peer solutions, plan delivery workstreams, document architecture, communicate technical concepts, and mentor junior engineers.

Designs and implements data quality frameworks across batch and streaming pipelines on an AWS data platform. Responsibilities include validation checks, observability, anomaly detection, alerting, data contracts, schema validation, root cause analysis, quality SLAs, metadata management, lineage, and governance. Partners with data engineers, analysts, and business stakeholders to improve the reliability, discoverability, and trustworthiness of sportsbook data supporting analytics, trading, and machine learning.

Lead AI enablement across Technology by coaching teams to identify, build, test, govern, and deploy AI agents and RAG solutions independently on KAIT. Deliver hands-on workshops, technical training, opportunity audits, and live troubleshooting. Embed security, data governance, compliance, documentation, and release standards into use cases. Route complex initiatives appropriately and support coding assistant adoption through GitHub Copilot and Cursor workshops.

Build and evolve a real-time trading risk platform for sportsbook operations. Design distributed streaming components, process the full bet lifecycle, implement complex financial calculations, aggregate exposure, create alerting systems, and optimize high-throughput, low-latency services. Ensure event ordering, idempotency, consistency, scalability, and resilience while collaborating with trading and product teams. The role uses a Flink-based, event-driven architecture and integrates with trader-facing downstream systems.

Own and optimize Workday HCM configuration and administration, including business processes, security, reporting, automation, and data integrity. Support a Workday tenant merge, releases, testing, enhancements, issue resolution, stakeholder requirements, training, and project delivery across multiple modules. Ensure governance, risk, compliance, and timely resolution of non-compliance issues while improving People Systems efficiency and user experience.

Lead execution of the AI strategy across Player Account Management by owning AI tools, setting adoption and productivity KPIs, evaluating new solutions, and promoting secure best practices. Build a network of AI ambassadors and technical experts, organize training and Geek Sessions, maintain approved MCPs and Skills catalogs, support stakeholders, and collaborate with security and technology teams to drive AI adoption and continuous improvement.