Data Engineering 

Job Description

Responsibilities

• Define, document, and drive adoption of technical standards across the data engineering team — pipeline patterns, modelling conventions, integration approaches, code review expectations.
• Make architectural decisions for pipelines, modelling layers, and integration patterns. Review and align the team’s technical choices against those decisions.
• Contribute through reference designs, design reviews, and critical implementation decisions. You work alongside the team, not above it.
• Act as the senior technical reviewer for production-critical systems and design changes.

Production Reliability

• Own production reliability for data pipelines. That means incident prevention, post-incident reviews, and systematic reduction of recurring failures.
• Build observability into every pipeline: freshness checks, completeness validation, schema drift detection, lineage tracking. If it’s not observable, it’s not production-ready.
• Drive durable fixes from root cause. Patches buy time; your solutions are permanent.

Scalable Architecture

• Design dimensional models, medallion layers, and integration patterns that hold as volume and complexity grow — not just for today’s requirements, but for the next order of magnitude.
• Enforce data contracts between producers and consumers. SLAs, SLOs, and schema agreements are engineering deliverables, not handshake agreements.
• Automate scheduling, testing, deployment, and quality gates. Manual processes are debt; you pay them down systematically.

Team Technical Velocity

• Raise the team’s technical ceiling through design reviews, pairing, and code review standards that teach, not just gatekeep.
• Make the right path the easy path: documented patterns, reusable components, clear conventions. When a new engineer joins, they should be building correctly within their first week.
• Influence through architecture, standards, and mentoring. Your leverage is in what the whole team ships, not just what you build alone.

Governance by Design

• Embed PII handling, access controls, and auditability into the pipeline itself. Governance is code, not a checklist someone runs quarterly.
• Automate data quality checks and anomaly detection so compliance is continuous, not periodic.
• Treat data contracts and SLAs as first-class engineering deliverables with the same rigour as application code.

Are you interested in this position?
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