1 to 25 of 178 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 ❯

optimize SQL views, Python/R scripts, and API connectors as needed. Continually optimize data cleaning and anomaly detection processes. Help manage and advance our GCP infrastructure, particularly with BigQuery and GCP integrations. Own data quality and governance Enforce naming conventions, dataset groupings, versioning, and access standards. Maintain rigorous QA practices to ensure clean, reliable, and consistent data. Continuously More ❯

databases (e.g., MongoDB, Cassandra). Familiarity with cloud platforms (AWS, GCP, Azure) and services related to data storage and processing. Experience with data warehousing solutions like Amazon Redshift, Google BigQuery, or Snowflake. Excellent problem-solving skills and the ability to work independently and in a team environment. Strong communication skills and the ability to collaborate effectively with technical and More ❯

MLFlow, Kubeflow, Docker, Kubernetes, Terraform, GitHub Actions Data & Integration: SQL, NoSQL, Snowflake, Databricks, Airflow, MuleSoft, Boomi, REST APIs Cloud Platforms: AWS (SageMaker, Lambda), Azure (ML Studio), GCP (Vertex AI, BigQuery 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 ❯

usually have demonstrable experience with technologies in some of these categories: Languages: SQL, Python, Java, R, C#/C++/C Database: SQL Server, PostgreSQL, Snowflake, Redshift, Aurora, Presto, BigQuery, Oracle DevOps: git, docker, subversion, Kubernetes, Jenkins Additional Technologies: Spark, Databricks, Kafka, Kinesis, Hadoop, Lambda, EMR Popular Certifications: AWS Cloud Practitioner, Microsoft Azure Data Fundamentals, Google Associate Cloud Engineer 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 ❯

Vertex AI, or similar frameworks. Implement MLOps best practices, including CI/CD for model retraining, versioning, and testing. Optimize performance and scalability of data storage solutions (e.g., Redshift, BigQuery, Snowflake, or Delta Lake). Ensure data quality, lineage, and governance through monitoring, validation, and documentation. Contribute to infrastructure-as-code (IaC) setups for reproducible deployments using Terraform, CDK 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 ❯

sqlmesh). Exposure to Python or R for data analysis and automation. Experience with statistical analysis, forecasting techniques, or basic predictive modeling. Familiarity with cloud data warehouses (e.g. Snowflake, BigQuery). Passion for data quality, continuous learning, and fostering a data-informed culture. Pay Range: $136,200.00 - $204,200.00 The pay range for this position is outlined above. The More ❯

sqlmesh). Exposure to Python or R for data analysis and automation. Experience with statistical analysis, forecasting techniques, or basic predictive modeling. Familiarity with cloud data warehouses (e.g. Snowflake, BigQuery). Passion for data quality, continuous learning, and fostering a data-informed culture. Pay Range: $136,200.00 - $204,200.00 The pay range for this position is outlined above. The More ❯

Strong experience as a GCP Data Engineer, with proven experience in designing and implementing data pipelines and solutions on the Google Cloud Platform. Proficiency in GCP services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Data Fusion. Experience in data ingestion, data transformation, and data modeling on GCP. Strong programming skills in Python or Java, and experience 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 ❯

and unstructured data Strong problem-solving skills with a business-focused approach Excellent communication skills Familiarity with LLMs Our Tech Stack Core Tools Data Infrastructure: Google Cloud Platform (GCP), BigQuery, Postgres BI/Analytics: Mixpanel, Explo Languages: Python, SQL Version Control: Git AI/ML Stack LLM Frameworks: Anthropic, OpenAI, Google LangChain Data Science & ML Libraries: pandas, scikit-learn 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 ❯

tools and offerings. Develop and optimize AI agents, utilize machine learning to determine patterns, and custom AI tools to address real-world challenges. Work with big data technologies like BigQuery to ensure data is readily available for model consumption and integration to reporting platforms. Collaborate with Analytics and AI teams to design world-class visualization and reporting solutions. Work 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 ❯

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 ❯