Senior Data Engineer

## About Us

Smirnov Labs is a fast-growing engineering company founded by an ex-Googler. We help product teams launch and scale quickly by leveraging modern technology and AI-assisted development.

Our projects are diverse — we partner with clients at the earliest stages and own the technical delivery end-to-end.

We're a small, senior-heavy team where every engineer has real impact. No bureaucracy, no hand-holding — just sharp people shipping fast.

This is an outstaff role: you'll be embedded directly with a client's engineering team as part of our delivery group.

## The Role

The client runs a custom AI system, and it's only as good as the data reaching it. Your job is to get that data in: connect to whatever third-party API holds it, pull it reliably, shape it, and keep it flowing.

That means a lot of integration work — every source has its own auth, its own pagination, its own rate limits, and its own idea of what a schema is. You'll own those connectors and the pipelines behind them end-to-end.

## What You'll Do

- Build and own integrations with third-party APIs — REST, GraphQL, webhooks, the occasional CSV drop or legacy SOAP endpoint. Auth flows, pagination, rate limits, retries, backfills.

- Design and run the pipelines that feed the client's AI system: ingestion, normalisation, enrichment, and delivery into the stores it reads from.

- Build the ingestion path for AI workloads — chunking, embeddings, and keeping vector indexes fresh as source data changes, without full re-indexing every time.

- Model the data. Design schemas that hold up as sources are added and change underneath you.

- Make pipelines idempotent, observable, and recoverable. Things will break upstream; the system should degrade predictably and tell you why.

- Own data quality: validation, schema-drift detection, reconciliation, alerting on the failures that matter rather than all of them.

- Ship and run your own work — Docker, CI/CD, cloud environments. You deploy what you build.

- Work directly with the client's engineering and AI teams on what the models actually need from the data — no layers in between.

- Use AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) as part of your daily workflow.

## What We're Looking For

- 5+ years building and running production data pipelines

- Strong Python and strong SQL — both at a level you'd defend in review

- Real depth in third-party API integration: OAuth and token refresh, pagination strategies, rate limiting, retries and backoff, incremental syncs, and handling APIs whose documentation is wrong

- Experience with a workflow orchestrator (Airflow, Dagster, Prefect, Temporal, or similar) and an opinion about it

- Solid Postgres: schema design, query performance, migrations. Warehouse experience (BigQuery, Snowflake, Redshift) a plus

- Batch and streaming ingestion patterns, and a sense of when each is the right call

- Comfortable owning your own deployments — Docker, CI/CD, cloud (AWS or GCP)

- A track record of shipping without a detailed spec and without process handed to you

- Comfort working in the US timezone (overlapping working hours required)

- Upper-Intermediate or higher English

- Ability to own and drive features independently with minimal supervision

## Nice to have

- Hands-on experience feeding LLM or RAG systems — chunking strategies, embedding pipelines, vector stores (pgvector, Qdrant, Pinecone, whatever you've used). Side projects count; commercial experience is not required here.

- dbt, or another transformation layer you've run in anger

- Data quality and observability tooling (Great Expectations, Monte Carlo, or your own)

- Experience with managed connectors (Fivetran, Airbyte) — including knowing when to stop fighting them and write your own

- Early-stage or agency background: greenfield work, shifting requirements, direct client contact

## What We Offer

- Competitive salary above market average

- Fully remote work

- Flat structure — work directly with the client's engineering team, no middle management

- Real technical ownership: you make the architecture decisions on the pipelines you build

- Diverse and technically challenging projects

- Modern AI-powered development workflow

- Small team culture — your voice matters, your code ships

- Paid vacation and sick leave

- Flexible schedule within the US timezone overlap

- Professional and career growth through real ownership, not courses and certificates

## How to Apply

Send your CV with a brief note about the nastiest API you've had to integrate — what made it hard, and how you made it reliable.