Data Engineer, Qflow

About the Role

We’re looking for a mid-to-senior engineer who is comfortable taking ownership of complex data infrastructure in a fast-moving startup. You bring engineering rigour to data problems, and you understand that the quality of what goes in determines the quality of what comes out. What matters most is your ability to build reliable, production-grade pipelines that real AI and analytics products depend on.

Who you are

• 4+ years of experience in a data engineering role, ideally in a product or SaaS environment
• Ability to think about data quality from an end user perspective – i.e. the value of our data and customer trust in data – as well as an internal perspective (validity, uniqueness, etc)
• Hands-on experience with Azure Cosmos DB, including data modelling for document-oriented workloads
• Experience with Pyspark
• Strong working knowledge of Microsoft Fabric or Azure Data Lake Storage, including experience designing medallion or equivalent layered architectures
• Solid SQL skills and experience with relational databases (PostgreSQL or similar)
• Proficiency in Python for pipeline development, transformation logic, and orchestration
• Experience building data pipelines that feed ML or generative AI workflows — understanding of what ‘good’ training and inference data looks like
• Familiarity with data quality practices: validation, monitoring, alerting, and lineage
• Working knowledge of Azure cloud infrastructure and services; exposure to Terraform or infrastructure-as-code is a plus
• Exposure to CI/CD practices and containerisation with Docker or similar
• Experience using AI coding tools to accelerate development while maintaining the ability to audit and correct LLM output for performance at scale
• Excellent communication skills, able to work across engineering, product, and non-technical stakeholders
• Comfortable with ambiguity and incremental delivery in a startup environment
• Nice to have: experience with Retool; familiarity with Medallion architecture

What the job involves

We’re looking for a Data Engineer to join our Data group. a cross-functional, high-impact team at the intersection of data engineering, machine learning, and data quality. The team designs, develops, and operates the scalable data infrastructure that powers [Employer hidden]’s platform and AI capabilities

Reporting to our Senior Engineering Manager and working closely with ML Engineers and Data Quality experts, you’ll own the pipelines that get the right data, in the right shape, to the right place. Here’s what you’ll do day to day:

• Design, build, and ingesting data from multiple sources into our data infrastructure (currently 100M+ rows and growing)
• Work with Azure Cosmos DB, Microsoft Fabric, and relational databases to model, store, and serve data at scale
• Build and manage data lake layers in Microsoft Fabric, including ingestion, transformation, and serving patterns that support both ML and analytical workloads
• Collaborate with ML Engineers to ensure training data is clean, versioned, and correctly structured — including pipelines that feed generative AI features
• Partner with Data Quality experts to implement validation, monitoring, and lineage tracking that give the team confidence in what flows through our systems
• Optimise pipeline performance, reliability, and cost; debugging failures quickly and building resilience in
• Contribute to data governance practices, including schema management, access controls, and documentation
• Maintain high standards in code quality, testing, and reproducibility, and share knowledge across the team
• Make informed trade-off decisions to manage the cost of Fabric compute

Application process

1. 30minute - Talent Screening

2. 45minute - Hiring Manager - CV Deep Dive

3. 90minute - Technical Interview

4. 30minute - SLT intro