Specialists who streamline and automate data workflows. A critical role in ensuring data is accessible, reliable, and actionable.
DataOps Engineers are the backbone of modern data infrastructure, focusing on the automation, monitoring, and optimisation of data pipelines. They work closely with data scientists, analysts, and engineers to ensure that data is efficiently processed, stored, and made available for business insights. Companies ranging from scaleups to the larger consultancies rely on DataOps Engineers to build robust and scalable data platforms.
A typical week for a DataOps Engineer is a mix of coding, testing, and collaboration with cross-functional teams.
A typical UK progression for dataops engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assists in the design and implementation of data pipelines, focusing on learning and contributing to small projects.
Owns the development and maintenance of data pipelines, ensuring data quality and performance.
Leads complex data infrastructure projects, mentors junior team members, and drives innovation in data processing.
Strategises and oversees the entire data infrastructure, influencing company-wide data policies and practices.
There's no single route, but most people follow some version of these steps.
Start with foundational knowledge in data engineering, including SQL, ETL processes, and data warehousing.
Work on real-world projects to build and maintain data pipelines, using tools like Apache Kafka and Apache Airflow.
Focus on automation, monitoring, and optimisation of data workflows, becoming proficient in DataOps best practices.
Take ownership of complex data infrastructure projects, ensuring data quality and performance at scale.
Mentor junior engineers and drive innovation in data processing, contributing to the company's data strategy.
Strategise and oversee the entire data infrastructure, influencing company-wide data policies and practices.
We don't have any matching roles at the moment, but new jobs are added daily.
As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
This role involves developing and supporting enterprise data pipelines and OpenShift-based solutions, collaborating with cross-functional teams to deliver cloud-native applications, and enhancing platform performance. The developer will work on scalable, secure, and efficient data processing and operational transformation initiatives using Scala, Apache Spark, and OpenShift.
This role involves leading the technical architecture for an Azure platform, designing and building large-scale data pipelines, and integrating end-to-end data solutions. The focus is on cloud migration, data ingestion, and ensuring data quality and consistency.
This role involves leading the migration of IBM DB2 databases to AWS Aurora PostgreSQL, designing and implementing data models, and communicating complex data concepts to stakeholders. You will work 2-3 days per week onsite in Farnborough, collaborating with business and technology teams to deliver data architecture solutions.
You will design, develop, and support data integration, warehousing, and reporting solutions for a secure government programme. The role involves working with Oracle databases, ETL processes, and data quality, within a complex enterprise environment.
This role involves designing and implementing complex data flows, building robust data models, and developing cloud-based data infrastructure using GCP, BigQuery, Dataform, and Airflow. You'll work closely with developers, analysts, and other data professionals to ensure strong data quality and governance.
Common tools include Apache Kafka, Apache Airflow, AWS Glue, and Snowflake. The choice depends on the specific requirements of the project and the company's tech stack.
Gain experience in data engineering, learn DataOps tools and best practices, and seek opportunities to work on data pipeline projects.
DataOps focuses on the automation and optimisation of data workflows, while DevOps is about the continuous integration and delivery of software applications.
Key skills include programming (Python, SQL), data pipeline tools (Apache Kafka, Apache Airflow), and a strong understanding of data architecture and ETL processes.
Salary ranges can vary widely based on experience, location, and company size. For more detailed information, refer to the salary section on this page.
Post your role in 90 seconds and reach the specialist audience that already reads this page.
