1 to 25 of 117 BigQuery Jobs in the UK excluding London

experience in Python development for data workflows Experience building and maintaining ETL pipelines, ideally with Apache Airflow or a similar orchestration tool Hands-on experience with Google Cloud Platform (BigQuery, GCS, etc.) or another major cloud provider Good understanding of data modelling principles and the ability to translate business needs into technical solutions Strong collaboration and communication skills, with More ❯

years of experience in data engineering or analytics, including designing and delivering enterprise grade data solutions Strong hands-on experience with Snowflake (or comparable cloud data warehouses like BigQuery, RedShift, Synapse) including data modelling, performance tuning and cost management Familiarity with SQL best practices, ELT patterns, and modern data transformation frameworks such as dbt Competence in at least one More ❯

years of experience in data engineering or analytics, including designing and delivering enterprise grade data solutions Strong hands-on experience with Snowflake (or comparable cloud data warehouses like BigQuery, RedShift, Synapse) including data modelling, performance tuning and cost management Familiarity with SQL best practices, ELT patterns, and modern data transformation frameworks such as dbt Competence in at least one More ❯

years of experience in data engineering or analytics, including designing and delivering enterprise grade data solutions Strong hands-on experience with Snowflake (or comparable cloud data warehouses like BigQuery, RedShift, Synapse) including data modelling, performance tuning and cost management Familiarity with SQL best practices, ELT patterns, and modern data transformation frameworks such as dbt Competence in at least one More ❯

big data platforms. Knowledge of data modeling, replication, and query optimization. Hands-on experience with SQL and NoSQL databases is desirable. Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) would be beneficial. Data Platform Management: Comfortable operating in hybrid environments (cloud and on-prem). Experience integrating diverse data sources and systems. Understanding of secure data transfer protocols More ❯

big data platforms. Knowledge of data modeling, replication, and query optimization. Hands-on experience with SQL and NoSQL databases is desirable. Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) would be beneficial. Data Platform Management: Comfortable operating in hybrid environments (cloud and on-prem). Experience integrating diverse data sources and systems. Understanding of secure data transfer protocols More ❯

and architecture, with a focus on developing scalable cloud solutions in Azure, GCP or AWS. Data Modelling using Kimball, 3NF or Dimensional methodologies Analytics Engineering lean, with experience within BigQuery (GCP) with data modelling in DBT and mobile/telecoms industry experience would be beneficial, with deep DBT experience being highly beneficialDepth of knowledge and understanding across core orchestration More ❯

including APIs, databases, and flat files. Assist in the setup and optimisation of data infrastructure and storage solutions, such as data warehouses and data lakes, using platforms like Snowflake, BigQuery, or Azure Synapse. Help manage cloud-based infrastructure and services across AWS, Azure, or GCP environments. Learn to monitor data quality and integrity across systems and gain an understanding More ❯

and infrastructure as code •Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. •Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). •Knowledge of network protocols and programming around HTTP(s), SSH, TCP and UDP •Development experience within secure or classified environments particularly where compliance or restricted networks More ❯

and infrastructure as code •Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. •Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). •Knowledge of network protocols and programming around HTTP(s), SSH, TCP and UDP •Development experience within secure or classified environments particularly where compliance or restricted networks More ❯

and infrastructure as code •Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. •Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). •Knowledge of network protocols and programming around HTTP(s), SSH, TCP and UDP •Development experience within secure or classified environments particularly where compliance or restricted networks More ❯

and infrastructure as code •Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. •Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). •Knowledge of network protocols and programming around HTTP(s), SSH, TCP and UDP •Development experience within secure or classified environments particularly where compliance or restricted networks More ❯

engineering and architecture, with a focus on developing scalable cloud solutions in Azure, GCP or AWS. Data Modelling using Kimball, 3NF or Dimensional methodologiesAnalytics Engineering lean, with experience within BigQuery (GCP) with data modelling in DBT and mobile/telecoms industry experience would be beneficial, with deep DBT experience being highly beneficialDepth of knowledge and understanding across core orchestration More ❯

in data or analytics engineering, ideally within fintech, SaaS, or digital platforms Deep proficiency in SQL and hands-on experience with dbt Experience working with modern data warehouses (Snowflake, BigQuery, or Redshift) Familiarity with software engineering principles (Git, CI/CD, testing frameworks) Strong understanding of data visualisation and BI tools Confident communicator able to translate technical detail into More ❯

in data or analytics engineering, ideally within fintech, SaaS, or digital platforms Deep proficiency in SQL and hands-on experience with dbt Experience working with modern data warehouses (Snowflake, BigQuery, or Redshift) Familiarity with software engineering principles (Git, CI/CD, testing frameworks) Strong understanding of data visualisation and BI tools Confident communicator able to translate technical detail into More ❯

in data or analytics engineering, ideally within fintech, SaaS, or digital platforms Deep proficiency in SQL and hands-on experience with dbt Experience working with modern data warehouses (Snowflake, BigQuery, or Redshift) Familiarity with software engineering principles (Git, CI/CD, testing frameworks) Strong understanding of data visualisation and BI tools Confident communicator able to translate technical detail into More ❯

Data Science, or a related field. 5+ years of experience in data architecture, solution design, or similar roles. Strong experience with modern data platforms and technologies (e.g., Snowflake, Databricks, BigQuery, Azure/AWS/GCP data services). Deep knowledge of data modeling, APIs, event-driven architectures, and cloud-native data architectures. Proven ability to design and implement scalable More ❯

Data Science, or a related field. 5+ years of experience in data architecture, solution design, or similar roles. Strong experience with modern data platforms and technologies (e.g., Snowflake, Databricks, BigQuery, Azure/AWS/GCP data services). Deep knowledge of data modeling, APIs, event-driven architectures, and cloud-native data architectures. Proven ability to design and implement scalable More ❯

in data architecture, data modeling, and scalable storage design Solid engineering practices: Git and CI/CD for data systems Highly Desirable Skills GCP Stack: Hands-on expertise with BigQuery, Cloud Storage, Pub/Sub, and orchestrating workflows with Composer or Vertex Pipelines. Domain Knowledge: Understanding of sports-specific data types ( event, tracking, scouting, video ) API Development: Experience building More ❯

and automation. Strong background in data visualisation (Power BI/Looker/Looker Studio). Experience with regulated data environments and a disciplined approach to quality and documentation. Desirable: BigQuery, Snowflake, or Databricks; reverse-ETL tools; marketing attribution methods. Next Steps If you're a data scientist passionate about using data and making an impact, we would love to More ❯

with Cucumber or similar framework. Experience in Azure DevOps, integrating automated testing into a CI/CD pipeline using Jenkins or GitLab runner. Good knowledge in GCP Storage Buckets, BigQuery, Dataflow and Cloud Functions (any Cloud). Hands-on experience with one or more programming languages (Java/JavaScript). Experience with non-functional testing using JMeter or similar More ❯

and Python . Strong grasp of ETL/ELT pipelines , data modeling , and data warehousing . Experience with cloud platforms (AWS, Azure, GCP) and tools like Snowflake, Databricks, or BigQuery . Familiarity with streaming technologies (Kafka, Spark Streaming, Flink) is a plus. Tools & Frameworks: Airflow, dbt, Prefect, CI/CD pipelines, Terraform. Mindset: Curious, data-obsessed, and driven to More ❯

Deep technical expertise in software and data engineering, programming languages (python, java etc.). Understanding of orchestration (Composer, DAGs), data processing (Kafka, Flink, DataFlow, dbt), and database capabilities (e.g. BigQuery, CloudSQL, BigTable). Container technologies (Docker, Kubernetes), IaaC (Terraform) and experience with cloud platforms such as GCP. CI/CD: Detailed understanding of working automated CI/CD pipelines More ❯

Deep technical expertise in software and data engineering, programming languages (python, java etc.). Understanding of orchestration (Composer, DAGs), data processing (Kafka, Flink, DataFlow, dbt), and database capabilities (e.g. BigQuery, CloudSQL, BigTable). Container technologies (Docker, Kubernetes), IaaC (Terraform) and experience with cloud platforms such as GCP. CI/CD: Detailed understanding of working automated CI/CD pipelines More ❯

the delivery of clean, scalable data solutions that power high-impact research. Key Responsibilities Build and maintain data pipelines using modern tools on Google Cloud, including Python, Spark, SQL, BigQuery, and Cloud Storage. Ensure data pipelines meet the analytical and scientific needs of key applications. Deliver high-quality, production-grade code with testing and documentation. Develop, measure, and monitor More ❯