"You're gonna need a bigger pipeline."
(Chief Brody, if he'd been a data engineer)
So what are the three key parts of this role?
1. Build the platform
Right now the data team is one person. You'll be the second, and a true partner to the data lead rather than a pair of hands.
The foundations are in: Snowflake, Databricks, dbt and early LLM pipelines. Your job is to turn them into a platform that scales. This role leans towards infrastructure, distributed systems and platform engineering far more than data modelling.
2. Ship data products where the data is the feature
You'll build an agentic semantic layer, event-driven data products and customer-facing analytics that real customers rely on, plus zero-to-one work across streaming, governance and knowledge management. The data is messy and comes from external systems that never quite follow the rules, so making it trustworthy is half the fun.
3. Own it end to end
You build it, you run it. AWS infrastructure, infrastructure as code, on-call, incident response and production reliability. You'll also own large parts of the data function outright, from pipeline design to stakeholder-facing analytics.
Who you'd be working for
We can't name them yet, but here's the picture. They're headquartered in San Francisco, but this role is fully remote anywhere in the US.
They're an AI-first B2B software company fixing one of the clunkiest problems in business: how companies exchange data with each other. Every order, invoice and shipment notice between a brand, a retailer and a logistics provider has to pass between systems never designed to talk to each other. That used to mean months of manual setup per connection. This company's self-service platform, AI-powered rules engine and pre-connected network gets businesses live in minutes.
Hundreds of customers, from Fortune 500 enterprises to consumer brands you'd recognise from your own shopping cart
Hundreds of millions of transactions across thousands of trading partnerships
Ranked number one in its category across major industry reports
Backed by some of the best-known investors in Silicon Valley
Around 100 people, growing fast, with AI at the core of the product
Why is it a terrific place to work? The data genuinely is the business, so every improvement you make shows up directly for customers. It's big enough to have real scale and revenue, small enough that one great data engineer can change its trajectory. Leadership is close, feedback is fast and the hiring manager is one of the most engaged on the market.
What this role IS
It's for a full-stack data engineer. The whole data stack, from infrastructure all the way to the customer.
It's for someone who has taken a data platform from zero to one, then scaled it. Ideally you were one of the first data engineers at a startup and can point at what you built and the metrics that show it grew.
It's for a generalist with depth, as comfortable designing event-driven infrastructure as working out what stakeholders actually need.
And it's for someone who uses AI properly. The hiring manager wants concrete, measurable examples of how AI multiplies your output day to day. Not "I've tried Copilot". Actual workflow, actual numbers.
What this role is NOT
This is where most applicants fall down. Every point below comes from real candidate feedback.
Specialists deep in one corner (performance tuning, migrations, ontology) without owning the broader stack
Maintaining someone else's ETL, or writing SQL all day in a mature warehouse
Writing dbt models inside someone else's architecture, or building dashboards
Careers spent entirely inside large companies. Startup experience matters
Contractors or consultants who delivered a project and moved on
Fragmented project work with no production on-call or infrastructure as code
Anyone who can't explain how they use AI to work smarter. That alone has sunk strong Principal and Staff engineers
"Staff" or "Principal" on your business card doing the talking for you
A resume with no evidence of true ownership and zero-to-one delivery gets rejected on the spot, regardless of seniority or brand names. So do vague bullet points.
What you'll bring
5+ years building and operating data platforms
Data pipelines built as distributed systems
Production dbt or an equivalent transformation framework
Production Snowflake, Databricks, BigQuery or similar
Hands-on AWS (GCP or Azure fine), including on-call, incident response and infrastructure as code
Customer-facing or embedded analytics products you built and shipped
Time as one of the first data engineers at a startup that scaled, with numbers to prove it
For Staff level, evidence you've influenced technical direction and managed stakeholders
Bonus points for a numerical background (engineering, physics, maths or CS), streaming with Kafka or Kinesis, and messy unstructured data.
Why it's worth your time
Data engineer number two, not number 40. A lead who wants a partner. Data that is the product, not the plumbing. Fully remote anywhere in the US, up to USD $230K plus equity.
How to apply
Send your resume with a short note covering:
The data platform or product you're proudest of building from zero, and what happened to it
How you use AI in your daily engineering workflow, with a real example of the impact
Your US work authorisation status (see below) keiran@bigwavedigital.com.au
Work authorisation: please read before applying
This role is open only to
US citizens and Green Card holders already based in the United States.
There is no visa sponsorship of any kind.
No H-1B sponsorship, no H-1B transfers, no OPT or STEM OPT, no TN, no E-3, no L-1 and no future Green Card sponsorship. No exceptions.
If you will need sponsorship now or at any point in the future, we can't put you forward.
On LinkedIn, set the location to
San Francisco Bay Area
with workplace type
Remote
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