Senior Data Engineer I/II

Reposted 3 Days Ago
Atlanta, GA, USA
In-Office
130K-165K Annually
Senior level
Artificial Intelligence • Marketing Tech • Software
The Role
Own and scale Lahzo's production data infrastructure: build and maintain ETL pipelines and transformations, define monitoring and anomaly detection, onboard clients, ensure pipeline reliability, mentor engineers, and drive automation and cost/scale optimizations to keep data trustworthy and self-serviceable.
Summary Generated by Built In

At Lahzo, we’re on a mission to help companies with complex sales cycles and high customer acquisition costs unlock meaningful revenue growth through a combination of precision-targeted demand generation and autonomous AI agents. In short?  We’re conversion-obsessed, bringing together cutting-edge technology, deep go-to-market expertise, and a relentless focus on revenue outcomes.

Backed by a team of experienced founders and technologists, we're building a platform that doesn't just generate leads—it intelligently orchestrates the end-to-end buyer journey, reducing friction, increasing velocity, and turning intent into revenue.

We're seeking a Senior Data Engineer I/II to join our rapidly growing tech startup. Every client report, experiment, and business decision runs on our data. This role exists to own and shape that foundation as we scale. Your job is to make data available and trustworthy. That means building and maintaining ETL pipelines, writing data transformations and table logic, running data quality monitoring and anomaly detection, and onboarding new clients onto the full data infrastructure from day one.

We are looking for someone who finds deep satisfaction in infrastructure that just works — who thinks seriously about monitor coverage, catches problems before clients do, and takes pride in building systems that are reliable and built to last. You'll spend your first 90 days going deep on our stack, clients, and how everything fits together today. Beyond that, we expect you to diagnose data problems, identify the right levers, and drive improvements to the systems and processes that keep data quality high as we scale. You won't just keep the system reliable — you'll make it require less keeping: generalizing fixes into guardrails, designing for cost and scale before it bites, and turning manual processes into automated ones.

You'll set the patterns the rest of the team builds on and raise the bar for the engineers around you as we scale.

What You’ll Do

  • Architecture & patterns Design the ETL, transformation, and modeling patterns the team builds on. Make build-vs-buy calls and own the tradeoffs.
  • Mentorship & standards — Review PRs, set conventions, and level up the I/II engineers and analysts.
  • 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: define what we monitor and to what SLA — not just tune thresholds — and decide where to spend the coverage budget. 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 and own the architecture that makes it repeatable and increasingly self-service as we scale to dozens of clients.
  • 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 — Own the complex, ambiguous requests and build the self-serve tooling that keeps the routine queue off engineering's plate.What We’re Looking For
  • 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

Nice to Have

  • 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

Why Join Us

  • Real ownership, real scale - You will own the data infrastructure that powers hundreds of millions of ad impressions and thousands of AI agent conversations every month — across 10+ live client environments.
  • An interesting problem space - We sit at the intersection of AI agents, programmatic advertising, and multi-channel attribution. The data problems are genuinely hard and the stakes are real — every pipeline you own is tied directly to client revenue.
  • A stack worth mastering - BigQuery, Dataform, Terraform, Monte Carlo, Hex, Cloud Functions. Production workloads, real clients, no toy data. And we are actively building toward self-hosted AI infrastructure.
  • Small team, high context, fast growth - You will know every pipeline and why it exists. No mystery tables, no hand-offs to teams you have never met. And you will grow with the company as we scale into new verticals and client markets.

Compensation

  • Base: $130k - $165k
  • Equity: Participation in our equity award program.

Benefits 

Benefits include medical, vision, dental, unlimited PTO, a 401k and most of all, a collaborative, growth-focused, high-trust, high-performance environment where your ideas matter.

Timing 

We are looking to fill this position as soon as we find the right candidate.

Equal Opportunity Employer

Lahzo appreciates your interest in our company as a place of employment. It is Lahzo’s policy to provide equal opportunity for employment to all qualified employees and applicants, regardless of race, religion, religious affiliation, ancestry, citizenship status, marital status, familial status, sexual orientation, gender identity, color, creed, national origin, sex, age, disability, or veteran status or any other characteristic protected by local, state or federal law. This policy applies to all areas of employment including recruitment, placement, training, transfer, promotion, termination, pay, and other forms of compensation and benefits. Lahzo will provide reasonable accommodations to qualified individuals.

Skills Required

  • 5+ years hands-on data engineering with production data infrastructure ownership
  • Strong SQL (complex aggregations, window functions, multi-step transformations)
  • Production data transformation experience across multiple environments (dev/staging/prod)
  • Infrastructure as code experience to provision and manage cloud data infrastructure and permissions
  • Python for data engineering (ETL scripts, pipeline tooling, automation)
  • Designing data quality monitoring and alerting strategy (not just tuning thresholds)
  • End-to-end debugging skills across the data stack and systematic problem diagnosis
  • Experience with cost- and scale-aware design (partitioning, clustering, spend optimization)
  • Mentorship and setting engineering standards (review PRs, set conventions)
  • Experience building client onboarding infrastructure or multi-tenant/per-client isolation patterns
  • Familiarity with Dataform or dbt
  • Terraform on GCP
  • BigQuery (partitioning, clustering, cost optimization)
  • Data quality monitoring tools (Monte Carlo, Great Expectations, or similar)
  • Cloud Functions or Cloud Run for ETL pipelines
  • Experience with A/B experiment pipelines or marketing attribution models
  • Experience building self-serve analytics tooling (Hex or similar)
Am I A Good Fit?
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The Company
32 Employees
Year Founded: 2023

What We Do

Building industry-specific AI growth solutions to complete a perfect revenue circle: data-driven marketing driving customers to an AI sales agent. Consistent delivery, immediate access, and infinitely scalable.

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