1 to 25 of 173 dbt Jobs

meaningful outcomes. Technical expertise: Advanced Python skills for data engineering tasks. Experience building and maintaining web scraping pipelines. Strong SQL skills, with expertise in performance tuning. Strong proficiency with dbt for data transformations. Hands-on experience with Apache Airflow or Prefect. Proficiency with GitHub, GitHub Actions, and CI/CD pipelines. Nice to have: Experience with GCP (BigQuery, Dataflow, Composer More ❯

to know it today but to learn from the team as it makes up a large part of the stack). Good knowledge of Data Engineering tooling such as dbt or Spark. CDC tools like Debezium are a bonus. Build data systems with a software and infrastructure engineer mindset, including tested, scalable, resilient, fault tolerant, observable and "as code" practices. More ❯

and scientists working across Africa, Europe, the UK and the US. ABOUT THE ROLE Sand Technologies focuses on cutting-edge cloud-based data projects, leveraging tools such as Databricks, DBT, Docker, Python, SQL, and PySpark to name a few. We work across a variety of data architectures such as Data Mesh, lakehouse, data vault and data warehouses. Our data engineers More ❯

expertise with AWS services, including Glue, Redshift, Data Catalog, and large-scale data storage solutions such as data lakes. Proficiency in ETL/ELT tools (e.g. Apache Spark, Airflow, dbt). Skilled in data processing languages such as Python, Java, and SQL. Strong knowledge of data warehousing, data lakes, and data lakehouse architectures. Excellent analytical and problem-solving skills with More ❯

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 ❯

modelling techniques (star schema, data vault, dimensional modelling). Proficiency in Excel-based data workflows for various Agile Retail projects. Hands-on experience with data pipeline orchestration tools (Airflow, dbt, Prefect, or similar). Benefits: Unlimited holiday Annual Wellbeing Allowance Flexible work culture Monthly socials and events Complimentary snack bar Employer pension contribution If you're a data enthusiast ready More ❯

modelling techniques (star schema, data vault, dimensional modelling). Proficiency in Excel-based data workflows for various Agile Retail projects. Hands-on experience with data pipeline orchestration tools (Airflow, dbt, Prefect, or similar). Benefits: Unlimited holiday Annual Wellbeing Allowance Flexible work culture Monthly socials and events Complimentary snack bar Employer pension contribution If you're a data enthusiast ready 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 ❯

cloud based database technologies like Snowflake, BigQuery, Redshift Experience in open source technologies like Spark, Kafka, Beam Good understanding of Cloud providers – AWS, Microsoft Azure, Google Cloud Familiarity with DBT, Delta Lake, Databricks Experience working in an agile environment Here’s a taste of the perks we roll out for our extraordinary team members: 25 Days of Holiday, plus More ❯

cloud based database technologies like Snowflake, BigQuery, Redshift Experience in open source technologies like Spark, Kafka, Beam Good understanding of Cloud providers – AWS, Microsoft Azure, Google Cloud Familiarity with DBT, Delta Lake, Databricks Experience working in an agile environment Here’s a taste of the perks we roll out for our extraordinary team members: 25 Days of Holiday, plus More ❯

Iceberg. Experience with data warehouse design, ETL/ELT, and CI/CD pipelines (GitHub, CodeBuild). Knowledge of infrastructure as code, performance tuning, and data migration. Exposure to DBT, Docker, or Microsoft BI stack is a plus. What’s on offer: Opportunity to work on large-scale, high-impact data projects. A collaborative, supportive environment with scope to grow More ❯

Iceberg. Experience with data warehouse design, ETL/ELT, and CI/CD pipelines (GitHub, CodeBuild). Knowledge of infrastructure as code, performance tuning, and data migration. Exposure to DBT, Docker, or Microsoft BI stack is a plus. What’s on offer: Opportunity to work on large-scale, high-impact data projects. A collaborative, supportive environment with scope to grow More ❯

and enhancing data governance, this is your chance to play a vital role in a collaborative, fast-paced environment. What Youll Do: Design, build, and maintain efficient ETL pipelinesleveraging DBT or your tool of choiceto feed a BigQuery data warehouse. Transform raw data into clean, structured formats suitable for analysis. Collaborate across engineering and analytics teams to implement scalable data More ❯

e.g., S3, Glue, Lambda, Redshift, Athena, EMR). Experience designing and building data lakes and modern data platforms. Proficiency with Python, SQL, and data pipeline orchestration tools (e.g., Airflow, dbt). Strong understanding of data modelling, ETL/ELT processes, and distributed systems. Knowledge of data security, governance, and compliance best practices. Excellent leadership, communication, and stakeholder engagement skills. What More ❯

team members to deliver outcome Highly motivated, with strong problem-solving skills and business acumen Desirable Skills Experience with data visualization tools (e.g., Looker, Tableau, Power BI) Familiarity with dbt, Airflow, or other modern data tools Exposure to marketing or e-commerce industries Additional Information Publicis Groupe operates a hybrid working pattern with full time employees being office-based three More ❯

of Python and SQL (experience with Snowflake highly desirable) Knowledge of BI tools such as Superset, Tableau , PowerBI or similar is desirable Knowledge of orchestration tools such as Airflow, DBT or Google Cloud Dataflow is a bonus Analytical and problem-solving skills, with a deep curiosity for fraud detection Excellent attention to detail to ensure quality of project delivery for More ❯

of Python and SQL (experience with Snowflake highly desirable) Knowledge of BI tools such as Superset, Tableau , PowerBI or similar is desirable Knowledge of orchestration tools such as Airflow, DBT or Google Cloud Dataflow is a bonus Analytical and problem-solving skills, with a deep curiosity for fraud detection Excellent attention to detail to ensure quality of project delivery for More ❯

of Python and SQL (experience with Snowflake highly desirable) Knowledge of BI tools such as Superset, Tableau , PowerBI or similar is desirable Knowledge of orchestration tools such as Airflow, DBT or Google Cloud Dataflow is a bonus Analytical and problem-solving skills, with a deep curiosity for fraud detection Excellent attention to detail to ensure quality of project delivery for More ❯

Data Platform Engineer, you will support the design, implementation, and management of a decentralised data architecture based on Data Mesh principles. You will work with technologies such as Snowflake, dbt, Microsoft Azure, and Microsoft Fabric to build scalable, secure, and high-performing platform capabilities. Your work will empower domain teams to autonomously own, access, and manage their data via self More ❯

Tech Lead, Data Engineering Manager etc. Proven success with modern data infrastructure: distributed systems, batch and streaming pipelines Hands-on knowledge of tools such as Apache Spark, Kafka, Databricks, DBT or similar Experience building, defining, and owning data models, data lakes, and data warehouses Programming proficiency in Python, Pyspark, Scala or Java. Experience operating in a cloud-native environment (e.g. More ❯

Tech Lead, Data Engineering Manager etc. Proven success with modern data infrastructure: distributed systems, batch and streaming pipelines Hands-on knowledge of tools such as Apache Spark, Kafka, Databricks, DBT or similar Experience building, defining, and owning data models, data lakes, and data warehouses Programming proficiency in Python, Pyspark, Scala or Java. Experience operating in a cloud-native environment (e.g. More ❯

experience in building and managing data pipelines using cloud-native tools, ideally on Google Cloud Platform. Hands-on experience with BigQuery and data transformation frameworks such as Dataform or DBT is essential. A strong background in SQL (particularly PostgreSQL), ETL/ELT processes, and working with large datasets is required. Experience integrating and preparing data for business intelligence tools like More ❯

Tech Stack Cloud Data Warehouse - Snowflake AWS Data Solutions - Kinesis, SNS, SQS, S3, ECS, Lambda Data Governance & Quality - Collate & Monte Carlo Infrastructure as Code - Terraform Data Integration & Transformation - Python, DBT, Fivetran, Airflow CI/CD - Github Actions/Jenkins Business Intelligence - Looker Experience and Attributes we'd like to see Platform Engineering Expertise Extensive experience in platform engineering; designing, building 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 ❯

experience in data leadership – ideally within fintech, SaaS, or regulated tech environments. Technical depth across data engineering, analytics, or data science. Hands-on familiarity with modern data stacks – SQL, dbt, Airflow, Snowflake, Looker/Power BI. Understanding of the AI/ML lifecycle – including tooling (Python, MLflow) and best-practice MLOps. Comfortable working across finance, risk, and commercial functions. Experience More ❯