ability (Vue, React or Angular good but not necessary) Agile The following is DESIRABLE, not essential: AWS or GCP Buy-side Data tools such as Glue, Athena, Airflow, Ignite, DBT, Arrow, Iceberg, Dremio Fixed Income performance, risk or attribution TypeScript and Node Role: Python Developer (Software Engineer Programmer Developer Python Fixed Income JavaScript Node Fixed Income Credit Rates Bonds ABS … to be in the office 1-2 times a week. The tech environment is very new and will soon likely include exposure to the following: Glue, Athena, Airflow, Ignite, DBT, Arrow, Iceberg, Dremio This is an environment that has been described as the only corporate environment with a start-up/fintech attitude towards technology. Hours are 9-5. More ❯
and business value, while also promoting engineering best practices. Key responsibilities will include: - Designing and implementing scalable data warehouse solutions using Snowflake - Building efficient ELT/ETL pipelines using DBT and other modern tooling - Writing and optimising complex SQL queries for large datasets - Applying software engineering principles to data systems, including version control, testing, monitoring and CI/CD - Working … optimal solutions - Strong SQL skills with experience across relational and non-relational databases - Proficiency in a modern programming language such as Python, Java or Scala - Hands-on experience using DBT for pipeline development and transformation - Familiarity with cloud platforms such as AWS, Azure or GCP - Knowledge of orchestration tooling (e.g., Airflow, Dagster, Azure Data Factory, Fivetran) Desirable: - Experience deploying AI 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 ❯
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 ❯
Python Data Engineer AI FinTech (DBT, SQL) - London/Hybrid Were hiring on behalf of a rapidly growing AI FinTech who are building the next-generation data-driven solutions. This is a brilliant opportunity for a hands-on Python Data Engineer with 2+ years' experience to join a modern, low-firefighting environment focused on clever, high-quality development - not legacy … and transformations. Your work will influence how real-time financial data is captured, transformed, and utilised. Experience: Python (2+ years experience) : Strong scripting and implementation skills Confident with Pandas DBT : Regular use of DBT in current role (not just setup experience) Creating reusable DBT modules SQL : Comfortable writing, optimizing, and deploying SQL through DBT models Nice-to-Haves: Snowflake experience … side projects, contributions, or personal innovations) Why Join? Greenfield Development: No tangled legacy mess - help shape a clean, smart system from the ground up Modern Stack: Snowflake, DBT, Python, Airflow - no tech debt weighing you down Low-Firefighting Environment: Spend your time building, not fixing Quality Over Volume: Small but impactful data - live, intelligent, and real-time More ❯
The Role We are looking for a Senior Data Engineer to join our growing data team and play a key role in building scalable, modern data products using Snowflake, dbt Cloud, and the DataVault framework. This is an exciting opportunity for someone with a strong foundation in cloud-based data engineering to design and develop end-to-end data pipelines … a strategic asset, we'd love to hear from you. What you will be doing Designing, building, and maintaining robust, scalable data pipelines and data products on Snowflake using dbt Cloud Applying DataVault 2.0 principles and patterns to build a flexible, extensible data model that supports business agility and traceability Collaborating closely with data analysts, data architects, and fellow engineers … and delivery goals Proactively identifying, diagnosing, and resolving performance and data quality issues across the data stack Enforcing best practices for data transformation, documentation, testing, and version control using dbt Supporting the evolution of our modern data stack and mentoring junior team members as needed Implement CI/CD best practices for data transformation using dbt Cloud and Git-based More ❯
Engineer (Enterprise DataWarehouse Developer) Description: As a Data Engineer, you'll design and maintain data scrapers and data pipelines, design & optimize analytics & relational databases, and build analytics models using DBT and bespoke aggregation engines. You'll work closely with business stakeholders, other BI Developers and DataOps as well as System engineers to support both data and application integrations using bespoke … processes Strong experience in data ingestion, transformation & orchestration technology (ETL tools such as Informatica, Datastage, SSIS, etc ) or open source Meltano, Airbyte, and Airflow Proven experience with DBT (databuildtool) Proficiency with business intelligence tools (Power BI, Tableau, SAP BI, or similar). Integration & Programming Hands-on experience with API development and integration (REST/SOAP) Proficiency in at More ❯
understanding of data engineering principles and best practices, including data modeling, observable ETL/ELT processes, data warehousing, and data governance. - Proficiency in data manipulation languages (e.g., SQL/DBT) and programming languages relevant to data engineering (e.g., Python). - Experience with a variety of data processing frameworks and technologies, including cloud-based data services. Software Engineering Practices: - Experience with … 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 Nice to Have Experience Understanding of various data architecture paradigms (e.g., Data Lakehouse, Data Warehouse, Data Mesh) and their applicability to 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 Skills & Attributes We'd Like To See: Extensive experience in data engineering, including designing and maintaining robust dataMore ❯
deliver value-focused data solutions We'd love to talk to you if: You've got solid experience working with Python, SQL, Spar and data pipeline tools such as dbt or Airflow You're comfortable working across cloud platforms - especially AWS (Glue, Lambda, Athena), Azure (Data Factory, Synapse), or GCP (Dataflow, BigQuery, Cloud Composer) You have a good understanding of More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
Harnham - Data & Analytics Recruitment
with a London base (flexibility offered) High-impact role with a growing, values-driven data team Platform-focused, mission-led engineering Work with a modern cloud-native stack (Snowflake, DBT, Airflow, Terraform, AWS) What You'll Be Doing Serve as the technical lead for cross-functional data initiatives Define and champion best practices for building scalable, governed, high-quality data … across teams-product managers, analysts, ML engineers, and more What You'll Bring Extensive experience designing and building modern data platforms Strong skills in Python , SQL , and tools like DBT , Airflow , Fivetran Expertise in cloud services (ideally AWS ) and IaC tools like Terraform Deep understanding of data architecture , ELT pipelines, and governance A background in software engineering principles (CI/… both technical and non-technical stakeholders A collaborative mindset and passion for coaching others Tech Environment Cloud : AWS (Kinesis, Lambda, S3, ECS, etc.) Data Warehouse : Snowflake Transformation & Orchestration : Python, DBT, Airflow IaC & DevOps : Terraform, GitHub Actions, Jenkins Monitoring & Governance : Monte Carlo, Collate Interested? If you're excited about platform-level ownership, technical influence, and building systems that help people tell More ❯
The team you'll be working with: We are seeking a highly skilled and experienced Data Architect to join our dynamic team. The ideal candidate will have a strong background in the full data modeling lifecycle, including designing, implementing, and More ❯
insight. Your role is to improve their interaction with these tools, whether they are internally or externally developed. Some examples of this type of work: Improving our in-house dbt CLI wrapper to make it more user friendly and optimise runtimes Monitor tooling interaction with tools like Sentry or Datadog to identify areas for improvement Developing our internal BI tooling … experience; we have plenty of incredible developers at Octopus Energy who are willing to teach if you're willing to learn! Required experience: Python Git Nice to have: SQL dbt Github CircleCI Airflow Kubernetes Terraform A Cloud warehouse provider e.g. Databricks, GCP, Snowflake AWS We aren't necessarily looking for someone who is "10-out-of-10" in all these More ❯
reconciliation, and integration verification activities. Core skills and experience: Proven experience designing scalable data architectures in cloud and hybrid environments. Expertise in data modelling, SQL, and platforms like Snowflake, dbt, Power BI, and Databricks. Fluency in Python and knowledge of multiple cloud providers (AWS, Azure, GCP). Understanding of security principles including role-based access control. Experience with legacy-to More ❯
top of our list Google Cloud Storage Google Data Transfer Service Google Dataflow (Apache Beam) Google PubSub Google CloudRun BigQuery or any RDBMS Python Debezium/Kafka dbt (DataBuildtool) Interview process Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting More ❯
. Proficiency with Docker, Linux, and bash. Ability to document code, architectures, and experiments. Preferred Qualifications Experience with databases and data warehousing (Hive, Iceberg). Data transformation skills (SQL, DBT). Experience with orchestration platforms (Airflow, Argo). Knowledge of data catalogs, metadata management, vector databases, relational/object databases. Experience with Kubernetes. Understanding of computational geometry (meshes, boundary representations More ❯
Our products are recognised by industry leaders like Gartner's Magic Quadrant, Forrester Wave and Frost Radar. Our tech stack: Superset and similar data visualisation tools. ETL tools: Airflow, DBT, Airbyte, Flink, etc. Data warehousing and storage solutions: ClickHouse, Trino, S3. AWS Cloud, Kubernetes, Helm. Relevant programming languages for data engineering tasks: SQL, Python, Java, etc. What you will be More ❯
detail and care about the features they implement. What we need from you: At least 3 years of relevant data engineering experience Strong Python and SQL skills Experience with dbt Experience with AWS Experience working with a columnar database such as Redshift Strong Experience with ETL/ELT and the management of data pipelines Familiarity with snowplow Experience with DataMore ❯
Cloud Run, Cloud Monitoring & Logging, Dataplex, Beam, Tentacles and Pub/Sub; Fluent Python, SQL skills with real life project experience; Experience on orchestration tools such as Airflow and DBT; Experience with one of major analytical DWHs is plus: BigQuery, Redshift, Snowflake, Databricks, Synapse; Work experience with following technologies are noteworthy to mention and might be seen as bonus: AWS More ❯
Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the level of fairness and More ❯
Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the level of fairness and More ❯
There are opportunities for professional development, such as training programs, certifications, and career advancement paths. KEY RESPONSIBILITIES Design, develop, and maintain scalable data pipelines SQL, Azure ADF, Azure Functions, DBT Collaborate with analysts and stakeholders to understand their data needs, scoping and implementing solutions Optimising and cleaning of data warehouse, cleaning existing codebase and creating documentation. Monitor and troubleshoot dataMore ❯
There are opportunities for professional development, such as training programs, certifications, and career advancement paths. KEY RESPONSIBILITIES Design, develop, and maintain scalable data pipelines SQL, Azure ADF, Azure Functions, DBT Collaborate with analysts and stakeholders to understand their data needs, scoping and implementing solutions Optimising and cleaning of data warehouse, cleaning existing codebase and creating documentation. Monitor and troubleshoot dataMore ❯
There are opportunities for professional development, such as training programs, certifications, and career advancement paths. KEY RESPONSIBILITIES Design, develop, and maintain scalable data pipelines SQL, Azure ADF, Azure Functions, DBT Collaborate with analysts and stakeholders to understand their data needs, scoping and implementing solutions Optimising and cleaning of data warehouse, cleaning existing codebase and creating documentation. Monitor and troubleshoot dataMore ❯
