Arrow Global Group

Purchases and manages non-performing loan portfolios

Azure Data Engineer

Data EngineeringUpdated on Nov 13

Full-timeManchester, UKNo salary listedHybrid

About the Job

Requirements

Experience building data pipelines as a Data Engineer or in a similar role.
Strong Python, PySpark, and SQL skills.
Hands-on experience with Microsoft Fabric, or with Databricks, Azure Synapse, or Azure Data Factory and willingness to move to Fabric.
Comfort with Git, pull requests, and continuous integration and continuous delivery (CI/CD).
Understanding of data modelling, including dimensional and medallion approaches, and data quality practices.
A degree in Computer Science, Information Systems, Data Science, or a related field is preferred.
Experience in financial data domains, such as loan servicing, transactions, reference data, IBOR/ABOR, or in Real Estate, Credit, Banking, or NPL Asset Management is desirable.
Microsoft certification, such as DP-700 Fabric Data Engineer, is a plus.

Responsibilities

Build and maintain ingestion and transformation pipelines on Microsoft Fabric, including Data Pipelines, PySpark notebooks, and Dataflows Gen2, across Landing, Curate, and Enrich layers of the medallion architecture.
Onboard source systems by configuring reusable ingestion patterns, including Fabric mirroring from Snowflake and SQL Server, change data capture, and file and API loads, rather than writing one-off code.
Write and tune PySpark and T-SQL for Lakehouse and Warehouse workloads, including fact and dimension models in the enterprise data warehouse.
Implement data quality rules and validation pipelines, and investigate and fix data issues identified by data-quality checks and reconciliations.
Use the engineering workflow of Git-based development in Azure DevOps, pull requests, peer review, and CI/CD promotion from Development to User Acceptance Testing to Production.
Contribute to infrastructure as code using Terraform and to the orchestration configuration database using SQL projects and DACPAC as needed.
Work with Data Stewards, Data Analysts, and the Data Architect to understand source data and convert mapping specifications and business rules into working transformations.
Maintain documentation, including source-to-target mappings, transformation rules, and runbooks, to ensure the work is auditable and supportable.
Support the platform by monitoring pipeline runs, triaging failures, and escalating issues where needed.
Help prepare trusted datasets and Power BI semantic models for portfolio analytics, asset strategy, finance, and risk.

Desired Qualifications

Terraform or other infrastructure-as-code experience is a plus.

We are seeking highly skilled and experienced Azure Data Engineers to join a newly formed group concentrating on Data. Within this role you will be a key member of the team, working on a complex and challenging project within the Financial Services/Capital Markets industry. The primary focus of the role would be on building resilient, reusable Data Pipelines to extract, load, and transform raw data into a relational data model. The successful candidate will work across complex, multi-source datasets including loan servicing systems, property and valuation platforms, collections systems, and third-party data providers, delivering reliable and auditable data at scale.

Build and maintain ingestion and transformation pipelines on Microsoft Fabric (Data Pipelines, PySpark notebooks, Dataflows Gen2) across the Landing, Curate and Enrich layers of the medallion architecture.
Onboard new source systems by setting up the team’s reusable ingestion patterns (Fabric mirroring from Snowflake and SQL Server, change data capture, file and API loads). Do this through configuration rather than one-off code.
Write and tune PySpark and T-SQL for Lakehouse and Warehouse workloads, including fact and dimension models in the enterprise data warehouse.
Implement data quality rules and validation pipelines, and help investigate and fix data issues found by DQ checks and reconciliations.
Work through the team’s engineering workflow: Git-based development in Azure DevOps, pull requests and peer review, and CI/CD promotion from Dev to UAT to Production.
Contribute to the infrastructure-as-code (Terraform) and the orchestration config database (SQL projects / DACPAC) as needed.
Work with Data Stewards, Data Analysts and the Data Architect to understand source data and turn mapping specs and business rules into working transformations.
Keep documentation current, including source-to-target mappings, transformation rules and runbooks, so the work stays auditable and supportable.
Support the platform day to day: monitor pipeline runs, triage failures and escalate where needed.
Help prepare trusted datasets and Power BI semantic models for portfolio analytics, asset strategy, finance and risk.
Our tech stack
- Platform: Microsoft Fabric (Lakehouse, Warehouse, Notebooks, Data Pipelines, Dataflows Gen2, Mirroring, OneLake)
- Languages: Python / PySpark, Spark SQL, T-SQL
- Sources: Snowflake, SQL Server, files and third-party feeds
- Azure: Azure SQL Database, ADLS Gen2, Key Vault, Data Factory, Entra ID (service principals and managed identities)
- Engineering: Azure DevOps (Repos, Pipelines, Boards), Git, fabric-cicd, Terraform / HCP Terraform, SQL Database Projects
- Governance and reporting: Microsoft Purview (data quality), Power BI (semantic models)

Experience building data pipelines as a Data Engineer or in a similar role
Strong Python/PySpark and SQL skills
Hands-on experience with Microsoft Fabric, or with Databricks / Azure Synapse / Azure Data Factory and a willingness to move to Fabric
Comfortable with Git, pull requests and CI/CD. Terraform or other infrastructure-as-code experience is a plus
Understanding of data modelling (dimensional / medallion) and data quality practices
Works well in a team, takes feedback in code review, and is keen to learn and develop
Strong analytical, verbal and written communication skills
Degree in Computer Science, Information Systems, Data Science or a related field preferred
Experience in financial data domains (loan servicing, transactions, reference data, IBOR/ABOR) or in Real Estate, Credit, Banking or NPL Asset Management is desirable
Microsoft certification (e.g. DP-700 Fabric Data Engineer) a plus

About the Company

Arrow Global Group

Purchases and manages non-performing loan portfolios

Arrow Global Group buys and manages non-performing loan portfolios across Europe. It acquires distressed debt from banks and other lenders, then services and collects on the portfolios to recover value for creditors. The process uses an end-to-end debt management platform for asset acquisition, receivables servicing, analytics, and recovery actions across multiple jurisdictions. Its strategy focuses on a pan-European footprint and growth through acquisitions, with the goal of providing creditors scalable recovery solutions and increasing value through expanded portfolio reach and disciplined execution.

Company size: 501-1,000
Company stage: Debt Financing
Total funding: $400M
Headquarters: Manchester, United Kingdom
Founded: 2005

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