Data Engineer I/II
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Job Description
Data Engineer owning data infrastructure for Lahzo's data operations. Focused on building reliable pipelines and ensuring data quality in a startup environment.
Responsibilities:
- ETL pipeline development — Build and maintain data ingestion pipelines that move data reliably from source into the warehouse. Own the infrastructure end-to-end.
- Data transformation and table logic — Build and maintain transformation models — client-specific and shared. Handle schema changes, new table configurations, and the ongoing queue of transformation requests.
- Data quality and anomaly detection — Own data quality monitoring end-to-end: setup, threshold tuning, alert triage, and fixes. Extend coverage through assertions and automated alerting. Turn reactive monitoring into proactive coverage.
- Client onboarding infrastructure — Every new Lahzo client gets a dedicated cloud project, service accounts, permissions, and registered data pipelines. You own this process from infrastructure provisioning to first clean pipeline run.
- Pipeline reliability and debugging — Understand the full data flow from raw event ingestion through final reporting tables. Debug issues across the stack end-to-end.
- Ad hoc data requests — First responder for data requests from internal teams — confirming requirements, making schema or pipeline changes, and keeping the queue clear so the team stays focused on higher-leverage work.
Requirements:
- Hands-on data engineering experience — you have built and maintained production pipelines end-to-end, not just written queries
- Strong SQL — production-quality, comfortable with complex aggregations, window functions, and multi-step transformations
- Data transformation experience — you have built and maintained SQL-based transformation pipelines across multiple environments (dev / staging / prod)
- Infrastructure as code — you can provision and manage cloud data infrastructure, set up permissions, and debug access issues without hand-holding
- Python for data engineering — ETL scripts, pipeline tooling, and automation
- Data quality mindset — you understand what good pipeline health looks like, know how to set up monitoring, tune alerting thresholds, and drive issues to resolution
- Systematic debugger — when something breaks, you trace it end-to-end across the stack rather than stopping at the first symptom
- AI-fluent but grounded — you use AI tools to move faster and validate more thoroughly, and you still understand what is happening underneath. You are not chasing the next shiny tool instead of shipping.
- Motivated by technical impact — you want to be the person who truly understands the systems, and you see growing expertise as the path to more interesting and higher-impact work
Benefits:
- medical
- vision
- dental
- unlimited PTO
- remote-first environment
- a 401k
- collaborative, growth-focused, high-trust, high-performance environment where your ideas matter
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