Senior Analytics Engineer (Snowflake, dbt, SQL) | c. $200K + Equity

"Where we're going, we don't need dashboards. We need data everyone can trust."

Dr Emmett Brown, if he had spent 2015 in a data team instead of a DeLorean

Great Scott. Every company says its data is a strategic asset. Very few have someone whose actual job is to make that true. This is that job, and the time circuits are already on.

The company

Our client is a Series B, New York based company at the intersection of consumer finance and applied AI. They have built a platform that uses data and AI to fix a large, old and famously clunky corner of financial services, the kind of thing that has looked the same since 1955, and they have done it in a way that works better for the businesses using it and for the everyday people on the other end of it.

The numbers back it up. Tens of billions of dollars have moved through the platform, more than twenty million consumers have interacted with it, and it outperforms the traditional approach by a wide margin. Well known consumer finance brands are customers, they stay, and they expand. The team is around a hundred people in downtown Manhattan, now growing from a single product into a multi product platform. We can share more once we speak.

The role

As the company's Senior Analytics Engineer, you will own the layer that makes its data trustworthy. Not for one team. For everyone, including the AI agents that will increasingly be the ones asking the questions. Think of it as the flux capacitor. Nothing else works without it.

Data Engineering already lands raw data reliably in Snowflake. Your job starts there. You will build and govern the Snowflake native, dbt driven gold layer and semantic layer so that a metric means the same thing whether it appears on a Sigma dashboard, gets pulled into an internal tool, is surfaced in a Slack chatbot, or is answered by an LLM acting on someone's behalf.

This is a foundational hire. The company is betting that the future of analytics is fewer people writing one off queries and more trust built into the data itself. You will be the person who makes that bet pay off.

What you will actually do

You will own the gold layer and build the certified semantic layer that feeds Sigma, internal tooling and the chatbot and LLM interfaces coming next, all from a single source of truth. You will define and enforce data contracts and standardised metrics, and when two teams disagree about what a number means, you will settle it and make the answer stick. No more alternate timelines where revenue means three different things.

You will partner with Data Engineering on a revamp of client reporting, push to expand what data the company captures alongside Analytics, Support and Client Acquisition, and own cost management across dbt, Snowflake compute and the analytics tooling itself. 1.21 gigawatts is a lot of compute. Someone has to keep an eye on the bill.

And you will build well documented data products that let analysts, product managers, ops teams and eventually AI agents get correct answers without pinging a data scientist.

What we are looking for

You have five or more years in analytics engineering, data engineering or something very close to it. You have deep expertise in a modern cloud warehouse, ideally Snowflake, and your SQL is advanced, with a track record of modelling data for both flexibility and trust. You have designed, built or governed a semantic layer using the dbt Semantic Layer, Cube, LookML or similar, and you have defined metrics and data contracts that multiple teams actually adopted rather than quietly worked around.

Crucially, you have lived through cross team disagreement over metric definitions and come out the other side with a resolution people use. Pipeline only backgrounds, or ad hoc reporting without ownership of the analytics layer, will not get this thing to 88 miles per hour.

What will make you stand out

Experience building data products or context layers that serve LLM based or agentic consumers, not just BI dashboards, is a strong signal and matters a lot to this team. Hands on time with Sigma or Looker helps. A background blending analytics engineering with client facing or consulting work is a plus, as is time at a startup or high growth company.

The people who thrive here understand how a single modelling decision ripples through dashboards, client reports and AI agents, the way one small change in 1955 rewrites everything that comes after. They take initiative beyond their formal remit, they are curious about what clients need from data rather than only what they ask for, and they write clearly. This is a writing first engineering culture, and documentation is part of the work, not an afterthought.

The practical bits

New York City, hybrid, with in office days in line with company policy. Compensation is circa $200K USD base plus meaningful equity in a Series B company. Visa sponsorship and relocation are considered case by case. No DeLorean provided, but the subway is close.

Why this one is worth a look

Most analytics engineering roles ask you to keep the dashboards running. This one asks you to build the trusted foundation an AI driven company will run on for years, at the exact moment it moves from one product to many. Your future is whatever you make it, so make it a good one.

If you want your modelling decisions to matter, and you want to build for both humans and AI agents from day one, get in touch with Keiran Hathorn at Big Wave Digital for a confidential conversation.

Senior Analytics Engineer (Snowflake, dbt, SQL) | c. $200K USD + Equity

AI-driven fintech | New York City (Hybrid) | Series B | c. $200K base plus meaningful equity