Data Engineer

TL;DR

Data Engineer (SQL/Python): Designing, building, and operating scalable data pipelines and data products that support analytics and operational decision-making, with an accent on reliability, governance, data quality, and BI integration. Focus on implementing incremental processing and schema evolution, investigating complex data incidents, and building reusable architectures that balance performance, cost, and scalability.

Location: Leeds, United Kingdom

Company

hirify.global provides data and software services that connect the vehicle lifecycle and protect homes and digital identities through analytics, algorithms, and automation.

What you will do

• Design, build, and operate end-to-end ETL/ELT pipelines across ingestion, transformation, and serving layers.
• Architect reusable data pipelines and frameworks that balance performance, cost, scalability, and reliability.
• Implement incremental loads, schema evolution, data contracts, validation, monitoring, alerting, and incident response.
• Deliver production-ready datasets and data products aligned with BI, analytics, reporting requirements, and SLAs.
• Investigate complex incidents, perform root-cause analysis, and drive remediation and prevention.
• Review code and pipelines, define engineering standards, translate requirements into technical designs, and mentor junior engineers.

Requirements

• Strong ownership, structured problem-solving, communication, and continuous-improvement mindset.
• Experience designing analytical data models with schemas, keys, data grain, normalization, and performance considerations.
• Strong SQL and Python development skills, including modular design, logging, testing, and code review.
• Experience building reliable ETL/ELT pipelines with incremental processing, idempotent patterns, and schema evolution.
• Knowledge of data governance, lineage, traceability, data quality, security controls, masking, and compliance requirements.
• Ability to make architecture decisions across batch and streaming systems while balancing scale, latency, cost, and governance.

Culture & Benefits

• Innovative environment focused on cognitive solutions, insights, algorithms, and automation.
• Cross-functional collaboration with engineering, BI, analysts, architects, and business stakeholders.
• Emphasis on maintainable solutions, documentation, observability, shared standards, and engineering best practices.
• Opportunity to contribute to data products used across a global organisation serving customers in more than 90 countries.