1 to 25 of 96 dbt Jobs

of the AWS services like Redshift, Lambda,S3,Step Functions, Batch, Cloud formation, Lake Formation, Code Build, CI/CD, GitHub, IAM, SQS, SNS, Aurora DB - Good experience with DBT, Apache Iceberg, Docker, Microsoft BI stack (nice to have) - Experience in data warehouse design (Kimball and lake house, medallion and data vault) is a definite preference as is knowledge of More ❯

engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and More ❯

engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and More ❯

engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and More ❯

data tools such as Snowflake, Databricks, or BigQuery. Familiarity with streaming technologies (e.g., Kafka, Spark Streaming, Flink) is an advantage. Experience with orchestration and infrastructure tools such as Airflow, dbt, Prefect, CI/CD pipelines, and Terraform. What you get in return: Up to £60,000 per annum + benefits Hybrid working (3 in office) Opportunity to lead and mentor More ❯

policies. Maintain data dictionary. Maintain business level data model. Recommending and introducing new technology where needed. Core: Cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Data transformation technology such as DBT Visual Studio Code Python CI automation systems such as Jenkins A git-based source control system such as BitBucket Data Warehouse/Kimball methodology Data replication technology such as Fivetran More ❯

policies. Maintain data dictionary. Maintain business level data model. Recommending and introducing new technology where needed. Core: Cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Data transformation technology such as DBT Visual Studio Code Python CI automation systems such as Jenkins A git-based source control system such as BitBucket Data Warehouse/Kimball methodology Data replication technology such as Fivetran More ❯

platforms, time series databases, and tools like Grafana Understanding of MLOps pipelines and tools for automated model management and deployment Experience with orchestration tools such as Apache Airflow or dbt Application Process Ready to take the next step in your career with beON? Send your CV with the subject line "Data Science & ML Engineer." We respect your time - no cover More ❯

systems. Good understanding of security, compliance, and agile delivery practices. Passionate about learning new technologies and collaborating with teams, including mentoring junior engineers. Nice-to-have experience with Snowflake, DBT, Terraform, and Azure DevOps CI/CD pipelines. We are proud to be a Disability Confident Committed employer. If you have a disability and would like to apply to one More ❯

of experience in a data engineering or similar role. Proven expertise in building and managing ETL/ELT processes, data lakes, and data warehouses. Strong proficiency in Python, SQL, DBT, and modern data platforms (e.g., Snowflake, BigQuery, Databricks, or similar). Experience with cloud environments (preferably AWS, Azure, or GCP). Familiarity with orchestration tools like Airflow, Prefect, or Dagster. More ❯

Advanced proficiency in Python and SQL, with experience in spatial SQL and PostGIS. Familiarity with spatial tools and libraries (GeoPandas, QGIS) and feature engineering concepts. Experience with data modelling, dbt, and version control (Git). Knowledge of spatial datasets (MasterMap, AddressBase, Land Registry). Desired: Experience with WMS/WFS services, graph theory (NetworkX), GDAL, and Snowflake. Nice to have More ❯

Terraform) Linux, containers, Kubernetes, and serverless Javascript, Node.js, React and Flutter Knowledge of NoSQL and relational databases (AWS RDS) Solid understanding of networking concepts Integration Services (Informatica Cloud, Airflow, DBT, DLT) Knowledge with messaging solutions (EventBridge, SNS, SQS, Rabbit MQ, Kafka) Knowledge of Data warehousing solutions (Snowflake) Experience in working within compliance (e.g.: quality, regulatory - data privacy, GxP, SOX) and More ❯

SQL and dimensional data modelling (SCDs, fact/dim, conformed dimensions) Experience with PostgreSQL optimisation. Advanced Python skills ETL/ELT Pipelines: Hands-on experience building pipelines using SSIS, dbt, Airflow, or similar Strong understanding of enterprise ETL frameworks, lineage, and data quality Cloud & Infrastructure: Experience designing and supporting AWS-based analytical infrastructure Skilled in working with S3 and integrating More ❯

overall data strategy. The ideal person for this role will have a strong background in data engineering , with experience building modern data solutions using technologies like Kafka , Spark , Databricks , dbt , and Airflow . You'll know your way around cloud platforms (AWS, GCP, or Azure) and be confident coding in Python , Java , or Scala . Most importantly, you'll understand More ❯

overall data strategy. The ideal person for this role will have a strong background in data engineering , with experience building modern data solutions using technologies like Kafka , Spark , Databricks , dbt , and Airflow . You'll know your way around cloud platforms (AWS, GCP, or Azure) and be confident coding in Python , Java , or Scala . Most importantly, you'll understand More ❯

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, lineage, and integrity across the data lifecycle. Practical expertise in data and AI governance , including implementing frameworks for data privacy, ethical More ❯

Ensure high-quality raw datasets to enable accurate analytics and data modeling. Deploy and maintain data tools on Kubernetes (Airflow, Superset, RStudio Connect). Support Data Analytics initiatives through DBT , DevOps , and deployment governance. You will work closely with a small, focused team, contributing directly to strategic data initiatives. Candidate Profile: The ideal candidate will have: Significant experience in Data More ❯

business deliverables.* Collect, transform, and process datasets from various internal and external sources, ensuring data quality, governance, and integrity.* Implement efficient data models and schemas within Snowflake, and use DBT for transformation, orchestration, and workflow management.* Optimise ELT/ETL processes for improved performance, cost efficiency, and scalability.* Troubleshoot and resolve data pipeline issues swiftly and effectively across the data More ❯

exprience in developing architectural strategies, blueprints for hybrid and cloud-native solutions ELT/ETL Frameworks & Pipelines Essential Develop robust ELT/ETL pipelines using tools like Apache Airflow, DBT, AWS Glue, Azure Data Factory, or Kafka Connect. Desirable Optimize data transformations for performance, reusability, and modular design (e.g., using SQL/Scala/Python). Disclosure and Barring Service More ❯

and in event-driven and eventual consistency systems using Kafka, .Net, Java, REST APIs, AWS, Terraform and DevOps Nice to have:experience in data pipping and modelling using SQL, DBT, ETL, Data Warehousing, Redshift and Python, and ecommerce and mobile applications background Additional Information Were a community here that cares as much about your life outside work as how you More ❯

foster a collaborative, forward-thinking culture where engineers work on large-scale datasets, distributed systems, and modern cloud platforms. Teams leverage cutting-edge tools such as Spark, Airflow, Kafka, dbt, and Databricks to build resilient, scalable, and high-quality data solutions. Why this role? ** Lead a talented team of data engineers delivering high-quality, reliable data systems. ** Shape the architecture More ❯

foster a collaborative, forward-thinking culture where engineers work on large-scale datasets, distributed systems, and modern cloud platforms. Teams leverage cutting-edge tools such as Spark, Airflow, Kafka, dbt, and Databricks to build resilient, scalable, and high-quality data solutions. Why this role? ** Lead a talented team of data engineers delivering high-quality, reliable data systems. ** Shape the architecture More ❯

giving express consent for us to process (subject to required skills) your application to our client in conjunction with this vacancy only. KEY SKILLS: GCP | Python | SQL | MongoDB | Airflow | dbt | Terraform | Docker | ETL | AI | Machine Learning More ❯

Strong expertise in Azure cloud services and Databricks . Advanced proficiency in Python and SQL for data engineering. Experience with ETL/ELT tools and orchestration frameworks (e.g., Airflow, dbt). Strong knowledge of data warehousing and cloud-based data architectures. Understanding of BI tools such as Power BI or Tableau. Strong problem-solving and debugging skills. Ability to communicate More ❯

You'll Bring 6+ years of experience in data engineering or similar roles. Proficiency in Python and SQL for data processing and performance tuning. Hands-on experience with Databricks, dbt, Airflow, or other modern data orchestration tools. Strong grasp of data modeling, distributed systems, and cloud data services. Familiarity with DevOps practices, including CI/CD, IaC (Terraform), and monitoring More ❯