1 to 25 of 259 BigQuery Jobs in the UK

such as MySQL, PostgreSQL, or Oracle. Experience with big data technologies such as Hadoop, Spark, or Hive. Familiarity with data warehousing and ETL tools such as Amazon Redshift, Google BigQuery, or Apache Airflow. Proficiency in Python and at least one other programming language such as Java, or Scala. Willingness to mentor more junior members of the team. Strong analytical More ❯

similar (for real-time data streaming) Experience using data tools in at least one cloud service - AWS, Azure or GCP (e.g. S3, EMR, Redshift, Glue, Azure Data Factory, Databricks, BigQuery, Dataflow, Dataproc Would you like to join us as we work hard, have fun and make history? Apply for this job indicates a required field First Name Last Name More ❯

One Big Table (OBT) methodologies. • Translate business requirements from stakeholders into robust, well-documented and tested dbt models. • Develop and own workflows within Google Cloud Platform environments, primarily using BigQuery and dbt. • Write high-quality, optimised SQL for data transformation and analysis. • Develop and maintain scalable data pipelines within the Google Cloud Platform, ensuring efficient and cost-effective data More ❯

use cases. Qualifications: 5+ years of experience in analytics engineering, data engineering, or a related field. Advanced SQL skills and experience architecting solutions on modern data warehouses (e.g., Snowflake, BigQuery, Redshift). Hands-on experience with advanced modelling techniques in dbt. A deep understanding of ETL/ELT processes and tools (e.g., Fivetran, Airbyte, Stitch). Experience with data 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 ❯

experience with cloud platforms (GCP, AWS, Azure) and their data-specific services Proficiency in Python, SQL, and data orchestration tools (e.g., Airflow, DBT) Experience with modern data warehouse technologies (BigQuery, Snowflake, Redshift, etc.) Strong understanding of data modeling, data governance, and data quality principles Excellent communication skills with the ability to translate complex technical concepts for business stakeholders Strategic More ❯

experience with cloud platforms (GCP, AWS, Azure) and their data-specific services Proficiency in Python, SQL, and data orchestration tools (e.g., Airflow, DBT) Experience with modern data warehouse technologies (BigQuery, Snowflake, Redshift, etc.) Strong understanding of data modeling, data governance, and data quality principles Excellent communication skills with the ability to translate complex technical concepts for business stakeholders Strategic More ❯

commercially viable, and aligned with client expectations. Enterprise Solution Design : Architect and lead the delivery of large-scale data platforms (including lakes, lakehouses, and warehouses) using GCP, Cloud Storage, BigQuery, Databricks, Snowflake. Cloud Data Strategy: Own cloud migration and modernisation strategy, leveraging GCP, and tools such as Terraform, Azure DevOps, GitHub, and CI/CD pipelines. Data Modelling: Apply More ❯

Streams)? Select Which statement best describes your hands on responsibility for architecting and tuning cloud native data lake/warehouse solutions (e.g., AWS S3 + Glue/Redshift, GCP BigQuery, Azure Synapse)? Select What best reflects your experience building ETL/ELT workflows with Apache Airflow (or similar) and integrating them into containerised CI/CD pipelines (Docker, GitHub More ❯

materials for clients Present findings and recommendations to senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks More ❯

materials for clients Present findings and recommendations to senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks More ❯

materials for clients Present findings and recommendations to senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks More ❯

materials for clients Present findings and recommendations to senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks More ❯

industry best practices to continuously enhance data engineering capabilities. Looking at our current pipeline of work, we can also consider those with an Analytics Engineering lean, with experience within BigQuery (GCP) with data modelling in DBT and mobile/telecoms industry experience would be beneficial. A bit about YOU! As much as we just love working with great, fun More ❯

Strong SQL Server skills, including query optimisation and performance tuning. Familiarity with scheduling and orchestration tools (e.g. Control-M). Hands-on experience with Google Cloud tools such as BigQuery, Composer, and VertexAI. Proficient in Python for data manipulation and modelling. Strong skills in data visualisation using Tableau Desirable: Background in insurance or financial services, with exposure to industry More ❯

tools (e.g., SSIS, Talend, Informatica). · Strong analytical thinking and problem-solving skills and excellent communication and stakeholder management abilities. · Experience with cloud data platforms (e.g., Azure, AWS, Google BigQuery). · Knowledge of Python or R for data analysis or automation and familiarity with ERP systems like IFS, SAP, NetSuite, or Microsoft D365. Financial modelling or FP&A experience More ❯

tools (e.g., SSIS, Talend, Informatica). · Strong analytical thinking and problem-solving skills and excellent communication and stakeholder management abilities. · Experience with cloud data platforms (e.g., Azure, AWS, Google BigQuery). · Knowledge of Python or R for data analysis or automation and familiarity with ERP systems like IFS, SAP, NetSuite, or Microsoft D365. Financial modelling or FP&A experience More ❯

version control best practices in a collaborative engineering environment. Strong communicator - able to engage with both technical and non-technical colleagues. Nice to Have Skills Experience working with GCP (BigQuery) or other modern cloud-native data warehouses (e.g. Snowflake, Redshift). Familiarity with data pipelining and orchestration systems (e.g. Airflow). Understanding of modern analytics architectures and data visualisation More ❯

version control best practices in a collaborative engineering environment. Strong communicator - able to engage with both technical and non-technical colleagues. Nice to Have Skills Experience working with GCP (BigQuery) or other modern cloud-native data warehouses (e.g. Snowflake, Redshift). Familiarity with data pipelining and orchestration systems (e.g. Airflow). Understanding of modern analytics architectures and data visualisation More ❯

used backend programming languages (Python, Node.JS, Java, PHP, GO, C#, C++) Familiar with version control tools and proper branching techniques (Gitlab preferred) Experience working with data warehouses (Google Cloud BigQuery), data governance, payments and treasury or capital markets systems So, what's in it for you? Our people are constantly striving to be the best through operational excellence. The More ❯

and non-technical stakeholders alike. The Head of Data Engineering & Insight will work within a modern, cloud-based BI ecosystem , including: Data Integration: Fivetran , HVR, Databricks , Apache Kafka, Google BigQuery , Google Analytics 4 Data Lake & Storage: Databricks Delta Lake, Amazon S3 Data Transformation: dbt Cloud Data Warehouse: Snowflake Analytics & Reporting: Power BI, Excel, Snowflake SQL REST API Advanced Analytics More ❯

to hierarchically structure information residing in the data under analysis. Robust knowledge of statistical concepts, accompanied by expertise with a set of analytical tools ranging from databases (e.g. SQL, BigQuery, noSQL) to programming languages (e.g. R, Python), and from data visualisation (e.g. Tableau, PowerBI) to machine learning. Understanding data engineering solutions is a plus. Strong experience in MLOps, including More ❯

of at least one database technology (relational, columnar, or NoSQL) and familiarity with others (e.g., MySQL, Oracle, MSSQL, Vertica, MongoDB) Knowledge of cloud data warehouses like Snowflake, Databricks, and BigQuery Proven ability to manage technical project delivery, including scoping, planning, and risk assessment Strong organisational skills to oversee multiple concurrent customer projects Adaptability to shifting customer requirements and priorities More ❯

of at least one database technology (relational, columnar, or NoSQL) and familiarity with others (e.g., MySQL, Oracle, MSSQL, Vertica, MongoDB) Knowledge of cloud data warehouses like Snowflake, Databricks, and BigQuery Proven ability to manage technical project delivery, including scoping, planning, and risk assessment Strong organisational skills to oversee multiple concurrent customer projects Adaptability to shifting customer requirements and priorities More ❯