1 to 25 of 157 BigQuery Jobs

Java, strong SQL skills, and knowledge of big data tools like Apache Hadoop, Spark, or Kafka. Experience with cloud platforms (AWS, Azure, GCP) and data warehousing solutions (Snowflake, Redshift, BigQuery) Self-driven and have demonstrated the ability to work independently with minimum guidance Demonstrated multitasking ability, problem solving skills and a consistent record of on time delivery and customer More ❯

and inspires and motivates the people behind it. Commercial acumen, with experience identifying and shaping new opportunities. Robust knowledge of statistical concepts and expertise across analytical tools: databases (SQL, BigQuery, noSQL), programming (R, Python, Spark), visualization (PowerBI, Tableau), and machine learning. Deep expertise in advanced analytics, data science, and traditional analytics, with a high-level understanding of data engineering More ❯

and inspires and motivates the people behind it Commercial acumen, with experience identifying and shaping new opportunities Robust knowledge of statistical concepts and expertise across analytical tools: databases (SQL, BigQuery, noSQL), programming (R, Python, Spark), visualization (PowerBI, Tableau), and machine learning Deep expertise in advanced analytics, data science, and traditional analytics, with a high-level understanding of data engineering More ❯

DevOps, integrating automated testing into a CI/CD pipeline using Jenkins or GitLab runner. Experience with either AWS, Azure OR GCP Cloud 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 ❯

Airflow, or dbt. Strong SQL skills and experience with cloud platforms (Azure preferred). Solid programming background in Python, Scala, or Java. Knowledge of data warehousing solutions (e.g. Snowflake, BigQuery, Redshift). Strong understanding of data governance, security, and compliance (experience within financial services is a plus). Leadership experience, with the ability to mentor, influence, and set technical More ❯

modeling concepts. Excellent analytical skills, attention to detail, and ability to interpret SAP business data. Preferred Skills Experience working with SAP extractors, IDOCs, orBW data flows. Familiarity with Snowflake, BigQuery,AWS Redshift, or other cloud data platforms. Exposure to data testing frameworks like Great Expectations or PyTest. Understanding of BI tools such as Power BI, Tableau, or SAP Analytics 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 ❯

to structure information residing in the data under analysis hierarchically. 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, Spark), and from data visualisation (e.g. PowerBI, Tableau) to machine learning. Deep expertise in advanced analytics, data science and traditional analytics. High More ❯

processing and analytics. Hands-on experience in data integration , including designing and optimising data pipelines (batch and streaming) and integrating cloud-based platforms (e.g., Azure Synapse, AWS Redshift, Google BigQuery ) with legacy systems, ensuring performance and scalability. Deep knowledge of ETL/ELT processes , leveraging tools like Apache Airflow, dbt, or Informatica , with a focus on ensuring data quality More ❯

AI/ML and AI enabled analytics (LLMs, RAG, agents). Strong hands on coding skills with Spark (PySpark/Scala), SQL, dbt, and modern data platforms (Databricks, Snowflake, BigQuery). Experience with cloud platforms (AWS preferred). Proven expertise in BI and semantic modeling using tools such as Power BI, Tableau, Looker, or Mode. Strong understanding of data 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 ❯

for guidance. Strong SQL skills and understanding of data models and data principles. Experience with mobile-responsive dashboards and self-service analytics. Desirable Experience with cloud platforms (Snowflake, Azure, BigQuery). Python skills. Experience in agile delivery environments. Understanding of GDPR and data governance. BI tool certifications. The BI team has historically been highly technical; you will bring the More ❯

functionally with engineers, analysts, and product managers to advance data platform capabilities. Own the roadmap for analytics features and data products across internal business units. Requirements Strong experience in BigQuery and DBT for data modelling and transformation. Advanced SQL skills and deep understanding of modern data architecture principles, especially Medallion architecture. Familiarity with orchestration tools such as Airflow. Experience More ❯

on cloud experience (AWS or GCP) with services for compute, data, and AI/ML. Proven skills in data engineering (ETL pipelines, APIs, SQL/NoSQL, data warehouses like BigQuery/Redshift). Experience with AI agents, LLMs and orchestration frameworks (LangChain, AutoGen, LlamaIndex, etc.). Demonstrated ability to ship production-ready internal tools or SaaS integrations. Strong problem More ❯

and problem-solving skills, with the ability to convert data into actionable insights. Proficiency in data visualization tools such as Power BI , or similar. Experience with Snowflake and GCP (BigQuery) or other cloud-based data warehousing technologies is highly desirable. Familiarity with Python is a plus. Working knowledge of Lead Scoring criteria, build and implementation is a plus Great More ❯

the following tools and technologies (note we do not expect applicants to have prior experience of all them): Google Cloud Platform for all of our analytics infrastructure dbt and BigQuery SQL for our data modelling and warehousing Python for data science Go to write our application code AWS for most of our backend infrastructure You should apply if: What More ❯

you’ll need: 5+ years in data analysis, including 2+ years with Looker or a similar BI platform. Strong SQL and data modelling experience. Understanding of data warehousing technologies (BigQuery, Snowflake, Redshift, etc.). Excellent analytical and problem-solving skills. Clear communication skills with the ability to present data concepts simply and effectively. High attention to detail and data More ❯

data processing frameworks (e.g., Spark, Flink). Strong experience with cloud platforms (AWS, Azure, GCP) and related data services. Hands-on experience with data warehousing tools (e.g., Snowflake, Redshift, BigQuery), Databricks running on multiple cloud platforms (AWS, Azure and GCP) and data lake technologies (e.g., S3, ADLS, HDFS). Expertise in containerization and orchestration tools like Docker and Kubernetes. More ❯

SQL . Hands-on experience with data orchestration tools (Airflow, dbt, Dagster, or Prefect). Solid understanding of cloud data platforms (AWS, GCP, or Azure) and data warehousing (Snowflake, BigQuery, Redshift). Experience with streaming technologies (Kafka, Kinesis, or similar). Strong knowledge of data modelling, governance, and architecture best practices . Excellent leadership, communication, and stakeholder management skills. More ❯

equivalent) - building models, tests, documentation, version control. Understanding of data warehousing concepts (star schemas, snowflake, slowly changing dimensions, partitioning, clustering). Experience working in a modern data stack (e.g. BigQuery, Snowflake, Redshift, Databricks, etc.) Comfortable working downstream (with BI/analytics users) and upstream (pipelines, ingestion) contexts. Familiarity with BI tools (we use Thoughtspot and Power BI). Proficient More ❯

Skills : Advanced hands-on experience with major cloud platforms (Azure, AWS, GCP), including cloud-native data services, orchestration, monitoring, and automation. Proven experience in GCP-based data platforms , including BigQuery, Dataflow, CloudSQL, Spanner, Pub Sub, Cloud composer and GKE. Data Engineering : Proficient in modern data tools and frameworks for building, operating, and optimising ETL/ELT pipelines, streaming data More ❯

Skills : Advanced hands-on experience with major cloud platforms (Azure, AWS, GCP), including cloud-native data services, orchestration, monitoring, and automation. Proven experience in GCP-based data platforms , including BigQuery, Dataflow, CloudSQL, Spanner, Pub Sub, Cloud composer and GKE. Data Engineering : Proficient in modern data tools and frameworks for building, operating, and optimising ETL/ELT pipelines, streaming data More ❯

technology solutions. Strong programming skills (JavaScript, TypeScript, Python, or similar). Experience with cloud platforms (preferably Google Cloud Platform) and serverless architecture. Advanced SQL and data warehousing skills (e.g. BigQuery). Familiarity with front-end frameworks such as React. Experience with ETL/ELT processes and data visualisation tools (e.g. Looker Studio). Knowledge of AI integrations and agent More ❯

if you Have 7+ years in data or backend engineering, including 2+ years in a lead or technical decision-making role. Are fluent in the modern data stack: DBT, BigQuery, Airflow, Terraform, GCP or AWS. Bring strong software engineering skills: Python, SQL, CI/CD, DevOps mindset. Understand data warehousing, ETL/ELT, orchestration, and streaming pipelines. Thrive in More ❯

similar role, with demonstrable expertise in designing enterprise-grade data platforms. Proficiency in modern data engineering and analytics technologies-for example, cloud platforms (e.g., Azure Synapse, AWS Redshift, GCP BigQuery), ETL frameworks, data modeling tools, and data governance platforms. Hands-on experience with both relational and dimensional modeling techniques, modern data lakehouse design, and scalable pipeline architecture. Exposure to More ❯