A data engineer builds and maintains the systems that move and store data: pipelines that collect data from applications and files, warehouses and lakehouses that organise it, and the quality checks and monitoring that keep it reliable for reporting, analytics and AI.
Hire data engineers to build reliable pipelines, warehouses and lakehouses that power analytics and AI. Our engineers work with Azure Data Factory, Microsoft Fabric, Databricks, Snowflake, Spark and Airflow.
Every one of our data engineers is technically vetted, communicates clearly in English and works with sufficient time-zone overlap for daily collaboration. Choose full-time, part-time or hourly engagement, start quickly and scale your team as your roadmap evolves.
Skills our data engineers bring to your team from day one.
Share your requirements, review hand-picked profiles, interview the candidates you like and onboard within days. Your developer works as part of your team, using your tools and processes, while we take care of HR, infrastructure, security and replacements.
Engagement models: Full-time dedicated (around 160 hours per month), part-time (around 80 hours per month) and hourly or project-based arrangements.
Our data engineers work with Azure Data Factory, Microsoft Fabric, Synapse, Databricks and Spark, Snowflake, AWS Glue and Redshift; write SQL, dbt and Python/PySpark; use Kafka and Event Hubs for streaming; and manage pipelines as code with Git and CI/CD on Azure and AWS.
Billing is monthly, and you can add people or reduce hours with reasonable notice as your needs change. Data engineers often work with ETL developers, Power BI developers and data scientists. For a delivered platform instead of staff, see data science development services.
Ready to hire data engineers? Tell us about your data sources and platform, and we will share suitable data engineers. Share your requirements.
Common questions about hiring data engineers through Prominent AI Services LLP.
Azure, AWS and Google Cloud, with particular depth in Azure Data Factory, Synapse and Microsoft Fabric.
Yes. They diagnose bottlenecks, optimise queries and add monitoring and error handling.
Yes. You can add resources or reduce hours with reasonable notice as your project needs change.
Rates depend on experience, platform expertise and engagement model. We share rates with each profile before interviews.
ETL developers focus on building and maintaining data loads, often in specific tools. Data engineers design the wider platform, including architecture, streaming, performance and governance, as well as pipelines.
Yes. They optimise queries, storage formats, partitioning, scheduling and compute sizing, and set up cost monitoring.
Yes. They can plan and carry out a phased migration from Synapse, SQL Server or other platforms to Fabric, running old and new side by side until results match.
