Azure Data Engineer: Build Scalable Data Assets

Jato Dynamics; England; On-site; GBP 65,000 - 95,000; Full time

Job summary

JATO Dynamics seeks an Azure Data Engineer to join agile teams. You will design, build and operate data pipelines in Azure, transforming raw data into scalable data products for internal and external customers.

Responsibilities include data ingestion, warehousing, and collaboration with cross-functional teams to deliver platform increments, while adhering to DevOps practices and data governance.

Qualifications

• Minimum 2+ years hands-on experience designing and developing complex ETL/ELT pipelines in the cloud (Azure Data Factory and Azure Synapse).
• Experience preparing data mappings from high level requirements to solution level details.
• Strong understanding of data warehousing concepts in lake and traditional databases; end-to-end ETL lifecycle.
• Familiarity with automation, scripting and DevOps practices.

Responsibilities

• Ingest, warehouse, and store data at scale in the Azure cloud platform as JATO Data Assets.
• Build Data Products from Data Assets and provision to customers to satisfy commercial requirement.
• Collaborate with Operations, Architecture, Delivery, Product and Software Engineering to release platform increments.
• Contribute to solution level architecture and design.
• Own your release from development to production following DevOps methods.
• Improve ADF and Synapse pipelines design and best practices.

Skills

ETL/ELT pipelines

Cloud data engineering

Agile methodologies

Data analysis

SQL/NoSQL querying

CI/CD/DevOps

Python scripting

Data quality

Data warehousing

Stored procedures

Tools

Azure Data Factory

Azure Synapse

PySpark Notebooks

SQL Server

MySQL

CosmosDB

MongoDB

NoSQL

CI/CD tooling

Power Automate

Job description

JATO Dynamics seeks an Azure Data Engineer to join agile teams. You will design, build and operate data pipelines in Azure, transforming raw data into scalable data products for internal and external customers.

Responsibilities include data ingestion, warehousing, and collaboration with cross-functional teams to deliver platform increments, while adhering to DevOps practices and data governance.