practices for data infrastructure, fostering a culture of collaboration and knowledge sharing. (Required) Kubernetes and Orchestration: Manage and optimize Kubernetes clusters, specifically for running critical data processing workloads using Spark and Airflow. (Required) Cloud Security: Implement and maintain robust security measures, including cloud networking, IAM, encryption, data isolation, and secure service communication (VPC peering, PrivateLink, PSC/PSA). More ❯
data quality, or other areas directly relevant to data engineering responsibilities and tasks Proven project experience developing and maintaining data warehouses in big data solutions (Snowflake) Expert knowledge in Apache technologies such as Kafka, Airflow, and Spark to build scalable and efficient data pipelines Ability to design, build, and deploy data solutions that capture, explore, transform, and utilize More ❯
Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, ApacheSpark, Delta Lake and MLflow. To learn more, follow Databricks on Twitter ,LinkedIn and Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that More ❯
on technically/40% hands-off leadership and strategy Proven experience designing scalable data architectures and pipelines Strong Python, SQL, and experience with tools such as Airflow, dbt, and Spark Cloud expertise (AWS preferred), with Docker/Terraform A track record of delivering in fast-paced, scale-up environments Nice to have: Experience with streaming pipelines, MLOps, or modern More ❯
Employment Type: Full-Time
Salary: £110,000 - £120,000 per annum, Inc benefits
Java, TypeScript, Python, and Go Web libraries and frameworks such as React and Angular Designing, building, and maintaining CI/CD pipelines Big data technologies, such as NiFi, Hadoop, Spark Cloud and containerization technologies such as AWS, OpenShift, Kubernetes, Docker DevOps methodologies, such as infrastructure as code and GitOps Database technologies, e.g. relational databases, Elasticsearch, Mongo Why join Gemba More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Fortice Ltd
between the data warehouse and other systems. Create deployable data pipelines that are tested and robust using a variety of technologies and techniques depending on the available technologies (Nifi, Spark) Build analytics tools that utilise the data pipeline to provide actionable insights into client requirements, operational efficiency, and other key business performance metrics. Complete onsite client visits and provide More ❯
to solve complex client challenges Strong software engineering foundation in Python, JavaScript/TypeScript, SQL , and cloud platforms such as AWS, GCP, or Azure Familiarity with data technologies like ApacheSpark or Databricks , and a structured, analytical approach to problem-solving If you're passionate about building AI-powered applications that positively impact millions of people and businesses … love to hear from you. Especially, if you know your way around all the following: Langchain, Lang graph, Agentic Frameworks, LLarma Python, JavaScript, TypeScript, AWS/GCP/Azure, Spark/Data Bricks, FastAPI/Flask, RAG, LLMs, GenAI, AI Solutions, Data Pipelines, Microservices, Solution Design, Software Development, Restful APIs, Chatbots, CI/CD Github Actions, AI Engineering, Unit More ❯
its native tech stack in designing and building data & AI solutions Experience with data modeling, ETL processes, and data warehousing Knowledge of big data tools and frameworks such as Spark, Hadoop, or Kafka More ❯
computer vision. Hands-on with data engineering, model deployment (MLOps), and cloud platforms (AWS, Azure, GCP). Strong problem-solving, algorithmic, and analytical skills. Knowledge of big data tools (Spark, Hadoop) is a plus. More ❯
computer vision. Hands-on with data engineering, model deployment (MLOps), and cloud platforms (AWS, Azure, GCP). Strong problem-solving, algorithmic, and analytical skills. Knowledge of big data tools (Spark, Hadoop) is a plus. More ❯
Pydantic) for document processing, summarization, and clinical Q&A systems. Develop and optimize predictive models using scikit-learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks , MLflow , and cloud-native platforms (Azure preferred). Collaborate with engineering teams to More ❯
Ability to translate complex technical problems into business solutions. 🌟 It’s a Bonus If You Have: Experience in SaaS, fintech, or software product companies. Knowledge of big data frameworks (Spark, Hadoop) or cloud platforms (AWS, GCP, Azure). Experience building and deploying models into production. A strong interest in AI, automation, and software innovation. 🎁 What’s in It for More ❯
Ability to translate complex technical problems into business solutions. 🌟 It’s a Bonus If You Have: Experience in SaaS, fintech, or software product companies. Knowledge of big data frameworks (Spark, Hadoop) or cloud platforms (AWS, GCP, Azure). Experience building and deploying models into production. A strong interest in AI, automation, and software innovation. 🎁 What’s in It for More ❯
solutions in Python and related ML libraries Strong background in applied machine learning, model development and data engineering Experience with cloud environments (Azure, AWS, GCP) and tools such as Spark, Hive, Redshift Demonstrated ability to lead cross-functional teams and mentor junior practitioners Ability to communicate complex technical concepts clearly to non-technical audiences Bonus Points For Participation in More ❯
technical specialist, design and architecture experience - 7+ years of external or internal customer facing, complex and large scale project management experience - 5+ years of database (eg. SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis) experience - 3+ years of cloud based solution (AWS or equivalent), system, network and operating system experience PREFERRED QUALIFICATIONS - AWS experience preferred, with proficiency in a wide range More ❯
survey exchange platforms. Knowledge of dynamic pricing models. Experience with Databricks and using it for scalable data processing and machine learning workflows. Experience working with big data technologies (e.g., Spark, PySpark). Experience with online market research methods/products. Additional Information Our Values Collaboration is our superpower We uncover rich perspectives across the world Success happens together We More ❯
deep learning, GenAI, LLM, etc. as well as hands on experience on AWS services like SageMaker and Bedrock, and programming skills such as Python, R, SQL, Java, Julia, Scala, Spark/Numpy/Pandas/scikit, JavaScript Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace More ❯
for new and existing diseases, and a pattern of continuous learning and development is mandatory. Key Responsibilities Build data pipelines using modern data engineering tools on Google Cloud: Python, Spark, SQL, BigQuery, Cloud Storage. Ensure data pipelines meet the specific scientific needs of data consuming applications. Responsible for high quality software implementations according to best practices, including automated test More ❯
for new and existing diseases, and a pattern of continuous learning and development is mandatory. Key Responsibilities Build data pipelines using modern data engineering tools on Google Cloud: Python, Spark, SQL, BigQuery, Cloud Storage. Ensure data pipelines meet the specific scientific needs of data consuming applications. Responsible for high quality software implementations according to best practices, including automated test More ❯
in Python for data pipelines, transformation, and orchestration. Deep understanding of the Azure ecosystem (e.g., Data Factory, Blob Storage, Synapse, etc.) Proficiency in Databricks (or strong equivalent experience with ApacheSpark ). Proven ability to work within enterprise-level environments with a focus on clean, scalable, and secure data solutions. If you are the right fit - contact me More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Hunter Bond
in Python for data pipelines, transformation, and orchestration. Deep understanding of the Azure ecosystem (e.g., Data Factory, Blob Storage, Synapse, etc.) Proficiency in Databricks (or strong equivalent experience with ApacheSpark ). Proven ability to work within enterprise-level environments with a focus on clean, scalable, and secure data solutions. If you are the right fit - contact me More ❯
london, south east england, united kingdom Hybrid / WFH Options
Hunter Bond
in Python for data pipelines, transformation, and orchestration. Deep understanding of the Azure ecosystem (e.g., Data Factory, Blob Storage, Synapse, etc.) Proficiency in Databricks (or strong equivalent experience with ApacheSpark ). Proven ability to work within enterprise-level environments with a focus on clean, scalable, and secure data solutions. If you are the right fit - contact me More ❯
london (city of london), south east england, united kingdom Hybrid / WFH Options
Hunter Bond
in Python for data pipelines, transformation, and orchestration. Deep understanding of the Azure ecosystem (e.g., Data Factory, Blob Storage, Synapse, etc.) Proficiency in Databricks (or strong equivalent experience with ApacheSpark ). Proven ability to work within enterprise-level environments with a focus on clean, scalable, and secure data solutions. If you are the right fit - contact me More ❯
using RDBMS, NO-SQL and Big Data technologies. Data visualization – Tools like Tableau Big data – Hadoop eco-system, Distributions like Cloudera/Hortonworks, Pig and HIVE Data processing frameworks – Spark & Spark streaming More ❯
