Job Opportunities API

Working at CreateFuture CreateFuture is an AI-native consulting partner where people do work that matters and are supported to do it well. We work alongside organisations such as PayPal, adidas, NatWest, FanDuel and Money Saving Expert, building digital products and services that make a difference  while always putting people first. We’re a team of creators. We write code, shape delivery, build go-to-market strategies, develop AI solutions and create the practices that support our people. We work side by side with our clients, challenging what’s not working and helping them to build the future. Our commitment to craft, quality, and culture has helped us scale to over 600 people in just a few years. Our UK Benefits  35 days leave (including bank holidays).  Private medical insurance. Enhanced parental and adoption leave.  Financial coaching + 5% pension match. 40 hours of paid learning and development.  View our full list of UK benefits. CreateFuture is a Great Place to Work-Certified™ company and has won Best Workplaces UK multiple years in a row. Join us on our journey. Let’s create tomorrow, together, today. About the role and team:   Role overview CreateFuture is delivering the migration of an ML estate from Databricks to an AWS SageMaker-based MLOps platform, working alongside AWS. The Senior Data Engineer (ML) sits in the Databricks workstream team (Delivery Manager, Lead ML Ops Engineer, a second Senior Data Engineer ML, and 0.5 FTE Cloud/DevOps), building and migrating the data pipelines that feed model training and inference, and proving parity between the old and new platforms. This is hands-on delivery in a regulated iGaming environment: production pipelines, not notebooks. Key responsibilities Migrate ML data pipelines from Databricks (Spark/Delta Lake) to the SageMaker-based "golden template" architecture, working to the pattern set by the Lead ML Ops Engineer Build and amend feature engineering pipelines, feature store integrations, and data access layers (S3, Glue, Lake Formation) supporting migrated models Implement parity and statistical testing to prove migrated pipelines/models match Databricks outputs Handle data migration/integration between Databricks and AWS: storage, permissions, IAM alignment Work within CI/CD and IaC patterns for pipeline deployment; document runbooks and hand over to Evoke teams Collaborate daily with Evoke ML engineering, the CF team, and AWS ProServe counterparts Skills & experience Must-have Python / PySpark - Expert. Production data pipeline development, not analysis-only AWS data/ML stack - Advanced. S3, Glue and/or EMR, IAM basics;  AWS  ML stack SageMaker (Pipelines, Feature Store, Endpoints)  SQL — Advanced strongly preferred. ML pipeline experience. Pipelines feeding model training/inference — feature engineering, versioned datasets, reproducibility Git + CI/CD for data/ML workloads Terraform/CloudFormation/CDK - working knowledge Nice-to-have Databricks → AWS (or cross-platform) migration experience — the single strongest signal Parity/statistical testing methodology Data orchestration (Airflow, dbt, Step Functions) Data governance & compliance (PII/GDPR); regulated industry background (iGaming strongly preferred, but FS, banking considered) Soft skills Comfortable working to an established pattern at pace within a small delivery team Clear communicator with client stakeholders — must articulate their own experience specifically and confidently (see below) Consulting/client-facing delivery experience advantageous What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules, impact over activity, and real investment in your growth both professionally and personally. You’ll be part of a supportive, a