designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka). Leadership & Communication: Proven ability to lead cross-functional teams in ambiguous startup settings. Exceptional written and verbal communication skillsable to More ❯
to fast-paced startup environment, comfortable with ambiguity and evolving responsibilities Work Authorization: Must be eligible to work in US or UK Preferred Experience: Data orchestration tools (e.g. , Airflow, Prefect)Experience deploying, monitoring, and maintaining ML models in production environments (MLOps)Familiarity with big data technologies ( e.g. , Spark, Hadoop)Background in time-series analysis and forecastingExperience with data governance and More ❯
to fast-paced startup environment, comfortable with ambiguity and evolving responsibilities Work Authorization: Must be eligible to work in US or UK Preferred Experience: Data orchestration tools (e.g. , Airflow, Prefect)Experience deploying, monitoring, and maintaining ML models in production environments (MLOps)Familiarity with big data technologies ( e.g. , Spark, Hadoop)Background in time-series analysis and forecastingExperience with data governance and More ❯
writing clean, testable, production-grade ETL code at scale. Modern Data Pipelines: Experience with batch and streaming frameworks (e.g., Apache Spark, Flink, Kafka Streams, Beam), including orchestration via Airflow, Prefect or Dagster. Data Modeling & Schema Management: Demonstrated expertise in designing, evolving, and documenting schemas (OLAP/OLTP, dimensional, star/snowflake, CDC), data contracts, and data cataloguing. API & Integration Fluency More ❯
analysts and stakeholders, ensuring technical solutions meet business needs. Experience with data ingestion tools, like Fivetran. Advantageous Exposure to deploying applications with Kubernetes. Experience with Data Orchestrator tools (Airflow, Prefect, etc.) Experience with Data Observability tools (Montecarlo, Great Expectations, etc.) Experience with Data Catalog tools (Amundsen, OpenMetadata, etc.) Interview Process Call with the talent team Take home task Tech interview More ❯
of scalable training pipelines for large datasets. Working experience leading complex, cross-functional projects and influencing technical direction across multiple teams. Familiarity with modern workflow orchestration tools such as Prefect, Kubeflow, Argo, etc. Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring. Exceptional problem-solving skills, with a proven ability to navigate More ❯
role or equivalent Strong Python and SQL proficiency Proven experience with a modern data warehousing solution (e.g. Snowflake, Azure Data Factory) Experience with modern Python-based orchestration tools (e.g. Prefect) Familiarity with Git-based development workflows and CI/CD pipelines Package: Salary up to £85,000 (depending on experience) Discretionary bonus Hybrid (London) Comprehensive benefits package Lead Data Engineer More ❯
on Business, Marketing, Analytics, or Computer Science Experience with AWS Experience with Javascript Basic understanding of digital interaction technologies such as live chat, virtual agents/chatbots Experience with Prefect Experience building User Interfaces What You Should Know About This Team Our Proof of Concept team is known for its collaborative spirit, a strong desire to learn, and, most importantly More ❯
have experience with RAG (Retrieval-Augmented Generation) systems, vector databases, and embedding models for knowledge extraction. You can architect complex workflows. You have experience with workflow orchestration tools (Airflow, Prefect, Temporal) or have built custom pipeline systems for multi-step autonomous processes. You bridge science and engineering. You are comfortable with scientific computing libraries (NumPy, SciPy, pandas) and understand scientific More ❯
