Browse remote CI/CD opportunities and confirm accepted candidate locations before applying.
This page focuses on remote roles with visible candidate-location eligibility for developers, engineers, DevOps, data, QA, support, and other IT professionals.
Use CI/CD as a stack filter without changing the original role titles, comparing seniority, accepted countries, and application readiness before opening the full role page.
Cloudflare · Business Intelligence
Design and implement key features of our internal applications
Cloudflare · Business Intelligence
Deploy, manage & support ML Applications & Services on Kubernetes
Databricks · Professional Services Operations
Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration
Databricks · Professional Services Operations
Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration
Newbridge · Data Science
Architect end to end batch and Spark Streaming pipelines on cloud (AWS/GCP/Azure).
Fasset · Data Science
Platform & Infrastructure Management: Own the deployment, scaling, and orchestration of our core data stack using AWS Managed Workflows for Apache Airflow (MWAA).
Smart Working · Developer
Support and maintain data ingestion pipelines, including connectors that pull data from NHS systems, practice management systems, and laboratory or equipment sources using APIs and screen-scraping.
Gather AI · Data Science
Architect a greenfield, multi-layer data warehouse (raw, refined, serving) that separates analytical workloads from production OLTP traffic.
Saaf Finance
Design, build, and maintain production-grade AWS infrastructure using Infrastructure-as-Code (Terraform preferred).
Alimentiv
Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
Tech Holding
Design, develop, and maintain scalable data pipelines and ETL/ELT workflows on AWS.
MediaRadar · Data Science
Build and operate pipelines: Design, build, and optimize robust ETL/ELT pipelines that move and transform millions of daily data points reliably and efficiently.
Mrsool · Data Science
Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt to power analytics and business-critical applications.
KinderCare Education, LLC
Databricks-Native ML Development: Design, develop, and deploy machine learning solutions using Databricks technologies including PySpark, Spark SQL, MLflow, Feature Store, AutoML
Architect, upgrade, design, and build scalable infrastructure solutions leveraging Kubernetes, AWS, RDS (MySQL/Postgres), and modern distributed patterns.
Athos Commerce · Data Science
A highly collaborative team player who enjoys solving complex data problems • Passionate about building scalable and reliable systems
CertifID · Data Science
Design, implement, and maintain ETL/ELT pipelines that pull and merge data from multiple internal and external systems, ensuring clean, reliable, and scalable data flows across the organization.
Teamified
The Snowflake platform is scalable, performant, secure, cost-efficient, and trusted across the organisation.
Lead the design and implementation of scalable ML systems, including supervised, unsupervised, and LLM-based solutions
B.Tech, B.E., or Master's Degree in Computer Science or a similar discipline
Fora Financial · Data Science
Data platform architecture: ingestion patterns, warehouse design, environment strategy, orchestration, access governance, and reliability standards.
HighLevel
Architect & Scale: Design and build highly scalable and reliable backend services using Node.js, MongoDB, and ElasticSearch, ensuring optimal indexing, sharding, and query performance
OneSix · Data Science
Data Engineering & DevelopmentDevelop and maintain robust data integration pipelines (ETL and ELT) to process large volumes of financial data across cloud and on-premise environments.
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