Senior Data Engineer — Data Foundation

ABOUT THE ROLE

Elaiia's populations are grounded in real behavioural data, and the foundation that supplies it runs in production today. This role exists to extend it — wider coverage, stronger sources, and measures that hold up under the scrutiny a research team applies to its own instruments.

You'd work directly with behavioural scientists, survey methodologists and psychometricians on a problem their field has been sharpening for decades: how to get from a question worth answering to something you can actually observe, obtain and stand behind. They know what ought to be measured. You'd know what can be built — and the interesting work happens where those two don't line up.

The rest is engineering, and it's the part that decides whether any of it is usable: ingestion that doesn't break quietly, joins across datasets with nothing in common, deduplication, quality checks, and documentation honest enough that someone can tell what a source is and isn't good for without asking you.

This isn't a pipeline maintenance role, and it isn't a greenfield one. You'd inherit something that works and make it substantially better.

RESPONSIBILITIES

– Work with our researchers to turn questions into data we can actually obtain — and design how something gets measured when the direct route isn't available

– Establish that a measure tracks what it claims to, against known quantities, before anyone builds on it

– Extend and own ingestion at scale — APIs, partner feeds, files, licensed sources — with rate limits, schema drift and silent breakage treated as design inputs rather than incidents

– Design the joins: entity resolution, deduplication and schema harmonisation across sources with no keys in common

– Evaluate and acquire external datasets where buying beats building — sample evaluation, coverage and representativeness, pricing and licensing terms

– Make quality measurable: coverage, freshness and validation checks that fail loudly, and honest documentation of what each source is biased toward

– Hold provenance and licence terms as data rather than folklore — what we have, where it came from, what we're permitted to do with it, and for how long

YOU MAY BE A FIT IF

– 4+ years building production data pipelines — ingestion, ETL/ELT, integration of large messy datasets from sources you didn't control

– Strong SQL and Python

– Resourcefulness with data: you've built a dataset that didn't previously exist, out of sources that didn't obviously fit together

– Statistical literacy — sampling, bias, representativeness, and the difference between a measure that correlates and a measure that holds

– Entity resolution and deduplication on data with no shared identifiers

– Data quality instinct: you look at a new source and see the problem in it before the promise

– Comfort working close to research — you can take a methodological argument seriously and push back on it

– AI tools as standard development practice

STRONG CANDIDATES MAY ALSO HAVE

– Alternative data — from the buying side, the vendor side, or both

– Computational social science, quantitative social research, or any field where the quantity that matters is hard to observe directly

– Panel, transaction, survey or consumer-behaviour data specifically

– Data licensing agreements negotiated — and then lived with

– Privacy and compliance in practice: GDPR, Swiss FADP, consent, purpose limitation, processor terms

– Having built the dataset that turned out to be the reason a product worked

This role suits someone happier with a measure they can defend than one that's convenient. If you've ever told a team that the dataset they were excited about wouldn't support the claim they wanted to make, you'll recognise the job.

ABOUT DELTA LABS

Delta Labs uses AI to simulate and predict consumer behaviour at scale. We build Elaiia, a simulation engine that generates AI Twins — intelligent synthetic agents that mirror real consumer populations. Our clients use Elaiia to simulate customer decisions before committing to them: pricing strategies, product launches, campaign messaging, channel allocation. We replace surveys, focus groups, and intuition with simulation-based evidence.

We're a small, focused team and we intend to stay that way. We give people ownership, trust, and the autonomy to do their best work. We work with urgency and intellectual honesty and expect new team members to match our pace. We seek individuals who are curious, rigorous, and want their work to have demonstrable impact. If you're drawn to the idea of a small team building something that hasn't existed before, let's build together.

THE STACK

TypeScript frontend (Next.js/React), PostgreSQL, Python microservices, durable background jobs, integrated LLM APIs, LangGraph agents with tracing. Deployed on Vercel and Microsoft Azure.

LOCATION

This role is based in Zürich, Switzerland. Delta Labs is an in-person company. Candidates are expected to be located in the Zürich area or open to relocation.

BENEFITS

Ownership of systems rather than tickets. Direct work with a research team of unusual depth — behavioural scientists, experimental economists, psychometricians and cognitive scientists whose methods you'd be building. A product global enterprises use for decisions that matter. The chance to shape an early-stage company.

Delta Labs is an equal opportunity employer, welcoming applicants of all backgrounds. If you need any accommodation during the process, tell us and we'll arrange it.