Senior Data Engineer I/II

Lahzo is a tech startup focused on helping companies with complex sales cycles unlock revenue growth through advanced technology. They are seeking a Senior Data Engineer I/II to own and shape the data infrastructure, ensuring data availability and quality while building and maintaining ETL pipelines.

Responsibilities:

• Design the ETL, transformation, and modeling patterns the team builds on
• Review PRs, set conventions, and level up the I/II engineers and analysts
• Build and maintain data ingestion pipelines that move data reliably from source into the warehouse
• Build and maintain transformation models — client-specific and shared
• Own data quality monitoring end-to-end: define what we monitor and to what SLA — not just tune thresholds — and decide where to spend the coverage budget
• Every new Lahzo client gets a dedicated cloud project, service accounts, permissions, and registered data pipelines
• Understand the full data flow from raw event ingestion through final reporting tables
• Own the complex, ambiguous requests and build the self-serve tooling that keeps the routine queue off engineering's plate

Requirements:

• 5+ years hands-on data engineering, with a track record of owning production data infrastructure end-to-end
• 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 strategist — you've designed monitoring and alerting strategy, not just tuned an existing one
• 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
• Cost- and scale-aware — you think about partitioning, clustering, and spend before it's a problem
• A force multiplier — you make the people and systems around you better
• Dataform or dbt
• Terraform on GCP
• BigQuery — partitioning, clustering, cost optimization
• Data quality monitoring tools — Monte Carlo, Great Expectations, or similar
• Multi-tenant or per-client data isolation patterns
• Cloud Functions or Cloud Run for ETL pipelines
• A/B experiment pipelines or marketing attribution models
• Hex or similar self-serve analytics tooling — building data products that non-technical teams can use independently