276 to 300 of 727 Remote Snowflake Jobs

effectively (git, bitbucket) The terminal is your second home! Demonstrable experience of building data pipelines Clear ability to prioritise tasks and drive them through to completion Nice to have Snowflake Python experience Terraform CI/CD #J-18808-Ljbffr More ❯

or Senior Data Solutions Architect Expertise in Kafka/Confluent , Databricks , Unity Catalog , and modern data lake/lakehouse architectures Strong grasp of cloud data platforms (AWS, Azure, GCP, Snowflake) Understanding of Data Mesh , Data Fabric , and data product-centric approaches Familiarity with Apache Spark , Python , and ETL/ELT pipelines Strong knowledge of data governance, lifecycle management, and compliance More ❯

Data Studio, Tableau, AWS QuickSight) Experience on Ecommerce and digital marketing metrics Expertise in using scalable analytics techniques (e.g., PySpark, Dask, Modin, Koalas) and MPP databases (e.g., BigQuery, Redshift, Snowflake) for complex data assembly and transformation What's in it for you? Work with tons of data, to which you will have full access Work closely with all functions within More ❯

and with external clients. Strong hands-on experience using SQL for multi-step/complex analytics is essential for this role. Experience in cloud platforms (GCP – BigQuery, Azure -Synapse, Snowflake) and exposure to data science tools/languages such as Python, dbt, D3, Github, GCP/AWS would be advantageous. (This is not a technical advanced data science role so More ❯

and with external clients. Strong hands-on experience using SQL for multi-step/complex analytics is essential for this role. Experience in cloud platforms (GCP – BigQuery, Azure -Synapse, Snowflake) and exposure to data science tools/languages such as Python, dbt, D3, Github, GCP/AWS would be advantageous. (This is not a technical advanced data science role so More ❯

automating manual processes. Required Qualifications & Skills Excellent proficiency in SQL is essential. Experience as an Analytics Engineer, Data Engineer, or similar roles. Experience with data warehousing technologies such as Snowflake, Databricks, and/or dbt. Experience in data pipeline and model development. Ability to build efficient, robust, and scalable solutions from requirements. Knowledge of orchestration and version control tools like More ❯

NBA, Formula 1, and Olympic events. Key Responsibilities: Design and build large-scale data warehouses, ETL pipelines, and reporting platforms that are robust and efficient. (Looking at going down Snowflake or BigQuery) Utilize your expertise in backend languages; the tech stack is flexible, including Java, Python, .NET, Ruby, and more. Implement strong coding principles, including CI/CD, TDD, and More ❯

Automate manual processes where possible. Required Qualifications & Skills Proficiency in SQL is essential. Experience as an Analytics Engineer, Data Engineer, or similar. Experience with data warehousing technologies such as Snowflake, Databricks, and/or dbt. Experience in data pipeline and data model development. Ability to build efficient, robust, scalable solutions from requirements. Knowledge of orchestration and version control tools like More ❯

and with external clients. Strong hands-on experience using SQL for multi-step/complex analytics is essential for this role. Experience in cloud platforms (GCP – BigQuery, Azure -Synapse, Snowflake) and exposure to data science tools/languages such as Python, dbt, D3, Github, GCP/AWS would be advantageous. (This is not a technical advanced data science role so More ❯

along with demonstrated knowledge of modern data engineering processes, concepts and life-cycle Experience using Python for data manipulation and transformation Experience using modern data platforms like Databricks or Snowflake to do the above Experience working with large-scale, event-driven data sets Love gaming and is interested in supporting the game development process Passionate about helping enable teams to More ❯

secure, scalable infrastructure designs. Build and maintain Kubernetes clusters, Terraform IaC, CI/CD pipelines , and observability tooling (Prometheus, Grafana). Optimise real‐time data pipelines using Apache Kafka, Snowflake, and Postgres —ensuring low‐latency, high‐reliability ingestion from IoT sensors and EHR integrations. Collaborate with our Security & Compliance team to uphold SOC 2, ISO 27001, HIPAA/NHS DSPT More ❯

secure, scalable infrastructure designs. Build and maintain Kubernetes clusters, Terraform IaC, CI/CD pipelines , and observability tooling (Prometheus, Grafana). Optimise real‐time data pipelines using Apache Kafka, Snowflake, and Postgres —ensuring low‐latency, high‐reliability ingestion from IoT sensors and EHR integrations. Collaborate with our Security & Compliance team to uphold SOC 2, ISO 27001, HIPAA/NHS DSPT More ❯

processes. Required Qualifications & Skills Excellent proficiency in SQL is essential. Experience as an Analytics Engineer, Data Engineer, Analytics Insight, or similar roles. Experience with data warehousing technologies such as Snowflake, Databricks, and/or dbt. Experience in data pipeline and data model development. Ability to build efficient, robust, and scalable solutions based on requirements. Knowledge of orchestration and version control More ❯

and with external clients. Strong hands-on experience using SQL for multi-step/complex analytics is essential for this role. Experience in cloud platforms (GCP – BigQuery, Azure -Synapse, Snowflake) and exposure to data science tools/languages such as Python, dbt, D3, Github, GCP/AWS would be advantageous. (This is not a technical advanced data science role so More ❯

reports, and self-service analytics tools. Developing and enhancing data collection procedures to include information that is relevant for building analytic systems, utilising tools such as WhereScape, Azure Synapse, Snowflake, AWS Redshift. Monitoring performance and advising any necessary infrastructure changes, utilising BI tools such as Qlik Sense Cloud, Power BI, or Tableau, as well as the AI, ML, and NL More ❯

Excellent data visualization and storytelling abilities. Ability to work in a multicultural environment with a partner-first approach. Nice-to-Haves: Experience with marketing, ecommerce, nonprofit analytics. Familiarity with Snowflake, Databricks, dbt, Heap, Amplitude, Salesforce. Working Environment: Standard office, some international travel, remote work possible. Criteria marked with ** are minimum requirements aligned with the UK Disability Confident Employer Scheme. UK More ❯

working with remote teams Bonus Skills Experience in cyber insurance or cyber security. Prior experience or willingness to learn how to work with large datasets and big data technologies (Snowflake, AWS Athena) 100% medical coverage, including out-patient care Life insurance 25+ paid holidays Annual home office stipend Mental and physical health wellness programs like Headspace, Wellhub Competitive compensation and More ❯

data. Proven experience in Pyramid Analytics for data visualisation and analytics development. Good knowledge of data modelling and cloud data warehouse concepts. Experience working with cloud data platforms (e.g., Snowflake, AWS Redshift, Google Big Query). Excellent communication and collaboration skills. The ability to thrive in a high-pressure, fast-paced environment. Something to note... Here at Snap, our values More ❯

remote teams Bonus Skills Actuarial qualification Experience in cyber insurance or cyber security. Prior experience or willingness to learn how to work with large datasets and big data technologies (Snowflake, AWS Athena) Perks 100% medical coverage, including out-patient care Life insurance 25+ paid holidays Annual home office stipend 7% employer pension contribution Mental and physical health wellness programs like More ❯

and Python (Pandas, NumPy preferred) Knowledge of statistical testing methodologies Experience with BI tools (Tableau, PowerBI preferred) Experience with cloud computing services & solutions (AWS, Azure, GCP, Amazon Marketing Cloud, Snowflake) Experience working with very large datasets and distributed data processing technologies (Spark, DuckDB etc...) We are only able to consider applicants with an existing right to live and work in More ❯

backend services and React frontends. You'll utilise tools such as Terraform for infrastructure-as-code ( IaC ), AWS (Lambda, EC2, EKS, Step Functions, VPC etc.) for ETL, Airflow pipelines, Snowflake, and ensure architectural alignment with AI/ML initiatives and data-driven services. You will serve as the go-to engineer for: End-to-end data migration architecture ( on-premise More ❯

goals. What We're Looking For Must Haves Real-world experience with Google Cloud and modern data stack tools such as dbt, Looker, Fivetran, Google BigQuery and/or Snowflake Great analytical, problem-solving and technical skills, a head for numbers and an attention to detail An excellent grasp of SQL, data modelling, cloud data warehousing and database design Awareness More ❯

Lake, playing a key role in providing support to the business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages of More ❯

Lake, playing a key role in providing support to the business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages of More ❯

AI libraries (e.g.scikit-learn, pandas, NumPy, SciPy, etc) Experience with ML frameworks such as TensorFlow, PyTorch, XGBoost, LightGBM, or similar Strong SQL skills and experience with data warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, Kubernetes, MLflow, Kubeflow, or similar platforms More ❯