26 to 50 of 193 BigQuery Jobs in the UK

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 ❯

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 ❯

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 ❯

data accuracy, availability, security, and compliance. Partner with leadership to shape the company's tech and data strategy. Experience: Proven experience in data engineering, analytics, and reporting (SQL, Google BigQuery, Power BI, Looker, Tableau). Strong capability with API integrations and low/no-code tools (n8n, Zapier, Airtable, Make). Broad technical problem-solving skills, able to implement 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 ❯

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 ❯

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 ❯

Experience Masters degree level Exposure to working with digital analytics tools (e.g. Google Analytics or Adobe Analytics) Knowledge of coding language or modelling tools relevant to data manipulation (e.g. BigQuery, SQL, R, Python, MatLab, Stata) Knowledge of dashboarding solutions (e.g. Power BI, Looker, Tableau) If this sounds like you, please get in touch! We look forward to meeting you. More ❯

focus on backend technologies and building distributed services. Proficiency in one or more programming languages including Java, Python, Scala or Golang. Experience with columnar, analytical cloud data warehouses (e.g., BigQuery, Snowflake, Redshift) and data processing frameworks like Apache Spark is essential. Experience with cloud platforms like AWS, Azure, or Google Cloud. Strong proficiency in designing, developing, and deploying microservices More ❯

solutions. Promote a culture of insight, simplicity, and innovation in data use. Skills & Experience Expert in Microsoft Power Platform; strong SQL skills. Experience with cloud analytics (e.g. Databricks, Snowflake, BigQuery). Skilled in automation, agile delivery, and stakeholder management. Strong understanding of data governance, CI/CD, and version control. Excellent data storytelling and visual communication skills. If you More ❯

particularly in data modelling. Strong SQL and hands-on dbt experience. Ability to convert business requirements into logical, scalable data models. Knowledge of cloud data platforms (e.g., Snowflake, Redshift, BigQuery). Strong communication and documentation skills. Structured, detail-oriented mindset. Desirable: Experience with semantic modelling tools (e.g., dbt SL, LookML). Familiarity with workflow orchestration and BI tooling. Version More ❯

Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and/or implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset....) SDLC and DevOps: Git, Docker, Jenkins/ More ❯

Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and/or implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset....) SDLC and DevOps: Git, Docker, Jenkins/ More ❯

Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and/or implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset....) SDLC and DevOps: Git, Docker, Jenkins/ More ❯

Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and/or implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset....) SDLC and DevOps: Git, Docker, Jenkins/ More ❯

Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and/or implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset....) SDLC and DevOps: Git, Docker, Jenkins/ More ❯

reinforcement learning, LLM orchestration, RAG systems, etc Familiarity with cloud services and MLOps tooling to deploy and scale data and ML workloads cost-effectively Familiarity with data warehouses (Redshift, BigQuery, Snowflake, etc) and best practices around data pipeline tools Strong proficiency in SQL Comfort with ambiguity and short feedback loops A passion for building products that make a real More ❯

Analysts, and cross-functional squads to land scalable data solutions. Mentor mid-level analysts on analytics delivery, tooling, and stakeholder engagement. Work with a modern data stack, including Redshift, BigQuery, Matillion, and Retool Location This role is based can be based in London or Nuneaton, and may be occasionally required to travel to any other location of H&B. More ❯

to deliver end-to-end data products. Mentor mid-level analysts and help strengthen capability across the Commercial Ops analytics team. Work with a modern data stack, including Redshift, BigQuery, Matillion, and Retool. Location: This is a hybrid role based in Nuneaton, with the requirement to be in the office at least two days per week. The Person What More ❯

Excellent written and verbal communication skills; presentation skills preferred Proficiency in Collibra APIs, Java, Groovy, and REST integrations. Strong SQL knowledge and familiarity with common databases (Oracle, PostgreSQL, DB2, BigQuery, etc.). Experience with ETL/ELT tools (Informatica, DataStage, etc.) and BI platforms (Qlik, Power BI, etc.). Understanding of metadata standards, data lineage, reference data, and business More ❯

Excellent written and verbal communication skills; presentation skills preferred Proficiency in Collibra APIs, Java, Groovy, and REST integrations. Strong SQL knowledge and familiarity with common databases (Oracle, PostgreSQL, DB2, BigQuery, etc.). Experience with ETL/ELT tools (Informatica, DataStage, etc.) and BI platforms (Qlik, Power BI, etc.). Understanding of metadata standards, data lineage, reference data, and business More ❯

of classical and modern ML techniques, A/B testing methodologies, and experiment design. Solid background in ranking, recommendation, and retrieval systems. Familiarity with large-scale data tools (Hadoop, BigQuery, Amazon EMR, etc.). Experience with BI tools and visualization platforms such as Tableau, Qlik, or MicroStrategy. Bonus: Experience with geospatial data and advanced analytics platforms. More ❯

of classical and modern ML techniques, A/B testing methodologies, and experiment design. Solid background in ranking, recommendation, and retrieval systems. Familiarity with large-scale data tools (Hadoop, BigQuery, Amazon EMR, etc.). Experience with BI tools and visualization platforms such as Tableau, Qlik, or MicroStrategy. Bonus: Experience with geospatial data and advanced analytics platforms. More ❯

of classical and modern ML techniques, A/B testing methodologies, and experiment design. Solid background in ranking, recommendation, and retrieval systems. Familiarity with large-scale data tools (Hadoop, BigQuery, Amazon EMR, etc.). Experience with BI tools and visualization platforms such as Tableau, Qlik, or MicroStrategy. Bonus: Experience with geospatial data and advanced analytics platforms. More ❯