51 to 75 of 217 Permanent BigQuery Jobs

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 ❯

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 ❯

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 ❯

Confluence; familiarity with Tableau/Power BI and basic Python. Understanding of ML tools (ML Studio, GitHub Copilot, Elastic Search), model explainability (SHAP, LIME), and cloud platforms (GCP including BigQuery, Vertex AI). Knowledge of data pipelines, ETL/ELT, and software/model development lifecycles. Experience with security, compliance (GDPR, data privacy), model governance, and risk management. Excellent 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 ❯

Additional Qualifications: Experience with design and deployment of data infrastructure on cloud environments, including AWS, Microsoft Azure, or Google Cloud, utilizing cloud native tools such as AWS Redshift, S3, BigQuery, or Databricks Experience with Docker and Kubernetes Experience implementing CI/CD best practices Master's degree in Data Engineering or a related field Clearance: Applicants selected will be 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 ❯

frontend engineering skills, especially in Python and JavaScript/TypeScript-based environments. Deep knowledge of data modeling, querying (SQL), and connecting analytics platforms to modern data warehouses (e.g., Snowflake, BigQuery ). Experience with authentication/authorization integration, multi-tenancy, and performance tuning in analytics applications. Cloud experience (AWS, Azure, or GCP) and familiarity with containerized deployments (Docker, Kubernetes). More ❯

5+ years experience as a Digital Analyst with experience of working in a Digital Analytics/data environment, working across a broad range of tools, extensive experience with SQL, BigQuery, Google Analytics (GA4), Visualisations tools like Power BI or Looker Studio. You will have excellent analytical skills with the ability to draw concise insights across multiple data sets, strong 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 ❯