76 to 100 of 411 Permanent Databricks Jobs

within the insurance sector, ideally with London Market experience. Strong knowledge of insurance data models, including policy, claims, and risk-related structures. Expertise in Azure Cloud Services (Data Factory, Databricks, Key Vault, Azure SQL) and data warehousing methodologies (Kimball, Inmon). Proficiency in T-SQL for data transformation and summarisation, with Python skills for building data pipelines. Advanced Excel skills More ❯

Experience building production-grade data pipelines (primarily in Python and SQL) in cloud environments, with an emphasis on scalability, code clarity, and long-term maintainability Hands-on experience with Databricks and/or Spark, especially Delta Lake, Unity Catalog, and MLflow Deep familiarity with cloud platforms, particularly AWS and Google Cloud Proven ability to manage data architecture and production pipelines More ❯

as causal inference (e.g., quasi-experimental and experimental designs) & machine learning methods (e.g., regression/classification); cloud-based services for DS (e.g., Microsoft Azure, Amazon AWS, Google Cloud Platform, Databricks or others); and (v) Able to travel domestically; potential for international travel. Job in Williamsburg, VA - remote work permitted with periodic travel to home office as needed. Full-time/ More ❯

Python and AI/ML frameworks such as PyTorch, LangChain, LangGraph, GraphRAG, and AutoGen. Experience with modern vector and graph databases (e.g., ChromaDB, Neo4j) and LLMOps platforms (e.g., Azure, Databricks, Azure OpenAI). Proven track record of delivering scalable AI solutions in enterprise settings, preferably in life sciences. Excellent communication and interpersonal skills, with the ability to lead projects and More ❯

Python and AI/ML frameworks such as PyTorch, LangChain, LangGraph, GraphRAG, and AutoGen. Experience with modern vector and graph databases (e.g., ChromaDB, Neo4j) and LLMOps platforms (e.g., Azure, Databricks, Azure OpenAI). Proven track record of delivering scalable AI solutions in enterprise settings, preferably in life sciences. Excellent communication and interpersonal skills, with the ability to lead projects and More ❯

Python and AI/ML frameworks such as PyTorch, LangChain, LangGraph, GraphRAG, and AutoGen. Experience with modern vector and graph databases (e.g., ChromaDB, Neo4j) and LLMOps platforms (e.g., Azure, Databricks, Azure OpenAI). Proven track record of delivering scalable AI solutions in enterprise settings, preferably in life sciences. Excellent communication and interpersonal skills, with the ability to lead projects and More ❯

models. Experience using version control tools such as Git, Bitbucket or similar central repository tools Familiarity with NoSQL Databases. Familiarity with JIRA preferred. Preferred experience with Azure: Data Factory, Databricks, Data Lake Storage, Analysis Services, and API - PySpark. Ability to multi-task in a fast-paced environment. Ability to prioritize tasks and projects using a browser-based tracking system. Ability More ❯

engineering roles, preferably for a customer facing data product Expertise in designing and implementing large-scale data processing systems with data tooling such as Spark, Kafka, Airflow, dbt, Snowflake, Databricks, or similar Strong programming skills in languages such as SQL, Python, Go or Scala Demonstrable use and an understanding of effective use of AI tooling in your development process A More ❯

strategy roles, ideally within a complex, regulated, or customer-centric environment Degree in a quantitative field (e.g., Data Science, Economics, Engineering, Business Analytics) Deep understanding of data platforms (e.g., Databricks, Tableau Cloud), data governance, and AI/ML applications in business contexts Strategic thinker with a bias for action and a passion for unlocking business value through data Excellent communication More ❯

Experience with go-to-market activities, RFI/RFP responses, and proposal materials. TOGAF or equivalent certification; cloud certifications (AWS/Azure); data & AI tooling (e.g., Azure Fabric, Snowflake, Databricks). Experience with Data Mesh and data governance platforms (e.g., Collibra, Informatica). Ability to develop a common language for data, data models, data dictionaries, vocabularies, taxonomies, or ontologies. Experience More ❯

Experience with go-to-market activities, RFI/RFP responses, and proposal materials. TOGAF or equivalent certification; cloud certifications (AWS/Azure); data & AI tooling (e.g., Azure Fabric, Snowflake, Databricks). Experience with Data Mesh and data governance platforms (e.g., Collibra, Informatica). Ability to develop a common language for data, data models, data dictionaries, vocabularies, taxonomies, or ontologies. Experience More ❯

Experience with go-to-market activities, RFI/RFP responses, and proposal materials. TOGAF or equivalent certification; cloud certifications (AWS/Azure); data & AI tooling (e.g., Azure Fabric, Snowflake, Databricks). Experience with Data Mesh and data governance platforms (e.g., Collibra, Informatica). Ability to develop a common language for data, data models, data dictionaries, vocabularies, taxonomies, or ontologies. Experience More ❯

Experience with go-to-market activities, RFI/RFP responses, and proposal materials. TOGAF or equivalent certification; cloud certifications (AWS/Azure); data & AI tooling (e.g., Azure Fabric, Snowflake, Databricks). Experience with Data Mesh and data governance platforms (e.g., Collibra, Informatica). Ability to develop a common language for data, data models, data dictionaries, vocabularies, taxonomies, or ontologies. Experience More ❯

Experience with go-to-market activities, RFI/RFP responses, and proposal materials. TOGAF or equivalent certification; cloud certifications (AWS/Azure); data & AI tooling (e.g., Azure Fabric, Snowflake, Databricks). Experience with Data Mesh and data governance platforms (e.g., Collibra, Informatica). Ability to develop a common language for data, data models, data dictionaries, vocabularies, taxonomies, or ontologies. Experience More ❯

e.g., Pandas, NumPy) and deep expertise in SQL for building robust data extraction, transformation, and analysis pipelines. Hands-on experience with big data processing frameworks such as Apache Spark, Databricks, or Snowflake, with a focus on scalability and performance optimization. PREFERRED QUALIFICATIONS: Solid understanding of cloud infrastructure, particularly AWS, with practical experience using Docker, Kubernetes, and implementing CI/CD More ❯

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 similar More ❯

like web applications on Azure cloud infrastructure with Docker, Kubernetes, automation testing and DevOps (ideally terraform + github actions). Create, monitor and maintain data pipelines and batch processes (databricks, pyspark, lakehouses, kafka). Productionise quant models into software applications, ensuring robust day-to-day operation, monitoring and back testing are in place. Stakeholder engagement: Elicit, validate and translate business More ❯

and manage ETL pipelines and APIs to improve data delivery and accuracy Contribute to the overall data strategy alongside BI and analytics colleagues Tech You'll Use SQL Python Databricks Experience with web scraping or similar techniques would be a bonus. Interview Process Initial interview with the Data Manager Technical discussion/assessment (you'll walk through your approach to More ❯

to meet business needs. Contribute to pre-sales, proposals, and thought leadership activities. Requirements Strong experience designing and maintaining data platforms and ETL/ELT solutions. Solid knowledge of Databricks, Python, PySpark, Spark SQL, Azure and/or AWS. Data modelling expertise (Inmon, Kimball, Data Vault). Familiar with DataOps practices and pipeline monitoring. Experience with sensitive data and applying More ❯

through advanced analytics and research-based problem solving. To be successful you should have: 10 years hands-on experience in AWS data engineering technologies, including Glue, PySpark, Athena, Iceberg, Databricks, Lake Formation, and other standard data engineering tools. Previous experience in implementing best practices for data engineering, including data governance, data quality, and data security. Proficiency in data processing and More ❯

through advanced analytics and research-based problem solving. To be successful you should have: 10 years hands-on experience in AWS data engineering technologies, including Glue, PySpark, Athena, Iceberg, Databricks, Lake Formation, and other standard data engineering tools. Previous experience in implementing best practices for data engineering, including data governance, data quality, and data security. Proficiency in data processing and More ❯

in BFSI or enterprise-scale environments is a plus. Preferred: Exposure to cloud platforms (AWS, Azure, GCP) and their data services. Knowledge of Big Data platforms (Hadoop, Spark, Snowflake, Databricks). Familiarity with data governance and data catalog tools. More ❯

in BFSI or enterprise-scale environments is a plus. Preferred: Exposure to cloud platforms (AWS, Azure, GCP) and their data services. Knowledge of Big Data platforms (Hadoop, Spark, Snowflake, Databricks). Familiarity with data governance and data catalog tools. More ❯

and assessing implications of user stories Essential Skills & Experience Software development experience in Java, Scala, or Python Hands-on experience with data platforms such as AWS, Azure, GCP, or Databricks Proficient in SQL and analytical query extensions Experience working with data formats like JSON and XML Familiarity with contemporary data stores (e.g. object stores, document stores, RDBMS, graph databases) Ability More ❯

managing complex financial datasets Python experience, particularly for data processing and ETL workflows Hands-on experience with cloud platforms- Azure Experience designing and maintaining data pipelines using tools like Databricks and PySpark Knowledge of data warehousing solutions - Snowflake experience would be brilliant Understanding of CI/CD processes for deploying data solutions Some exposure to big data technologies and distributed More ❯