Overview
- Engineering background: You bring 5+ years of experience as a Data Engineer, including production support, and know your way around Snowflake, dbt, PySpark and Airflow.
- Operations mindset: You are comfortable with incident management, root-cause analysis and SLA-driven support, and you debug DAG failures, retries and backfills with confidence.
- Stakeholder management: You manage expectations during incidents with timely updates and honest timelines, and you build trusted relationships with data, engineering and business teams.
- Communication: You explain technical issues and their business impact clearly to technical and non-technical audiences, and you write clear tickets and documentation.
- Nice to have: Experience with PagerDuty for alerting, on-call scheduling and escalation.
- Language skills: Strong verbal and written English.
Job Description
YOUR ROLE
- Keep pipelines healthy: You monitor and support production data pipelines built on Airflow, dbt and PySpark, and resolve failures within agreed SLAs.
- Resolve incidents for good: You triage issues, run root-cause analysis and implement permanent fixes instead of repeating workarounds.
- Optimise Snowflake: You maintain and tune Snowflake queries, models and warehouses for performance, reliability and cost.
- Own on-call and alerting: You respond to alerts, take part in the on-call rotation and escalate and coordinate with other teams when needed.
- Serve our stakeholders: You handle data requests, bug reports and enhancements, prioritise by business impact and keep stakeholders updated until closure.
- Prevent the next incident: You improve monitoring, testing and data quality checks, and write runbooks and incident reports so knowledge is shared across the team.
- Be a role model in the project team: You pass on your way of working in daily collaboration, reviews and pairing.
Skills & Requirements
Data Engineering, Production Data Pipeline Support, Apache Airflow, dbt, PySpark, Snowflake, SQL, Data Pipeline Monitoring, Production Support, Incident Management, Incident Triage, Root Cause Analysis (RCA), SLA Management, DAG Debugging, DAG Retries, Backfills, Snowflake Query Optimization, Snowflake Model Optimization, Warehouse Performance Tuning, Cost Optimization, Reliability Engineering, On-Call Support, Alerting, PagerDuty, Escalation Management, Stakeholder Management, Data Requests Management, Bug Resolution, Enhancement Management, Business Impact Prioritization, Monitoring, Testing, Data Quality Checks, Runbook Creation, Incident Reporting, Technical Documentation, Ticket Management, Cross-Functional Collaboration, Code Reviews, Pair Programming, Technical Communication, Business Communication, Problem-Solving, Written and Verbal English.
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