About Labrynth

Labrynth accelerates progress by streamlining regulatory complexity. We build AI-powered platforms that navigate complex regulations, generate audit-level documentation, and provide certainty, not shortcuts. Our technology serves clients across heavily regulated industries including energy, compliance, and government regulations.

We operate as a forward-deployed engineering organization: small, high-velocity teams embedded directly with clients to rapidly discover needs and ship production-quality solutions.

About the Role

We are hiring a Data Engineer to build the data platform behind our regulatory indices: acquiring fragmented public data, transforming it into clean, auditable datasets, and constructing the index methodology that turns it into published rankings.

This is a data platform role more than a pure pipeline or backend role. You will sit close to the raw sources and close to the math. The work spans three modes:

Acquire: source data from fragmented and often hostile places, including government open-data portals, APIs, HTML, PDFs, legacy Excel formats, login-protected portals, and commercial sites behind anti-bot protection.

Transform: normalize inconsistent jurisdictional data through bronze → silver → gold pipelines with idempotent ingestion, content hashing, and run-level lineage.

Construct: turn clean data into transparent, auditable indices through winsorization, percentile ranks, weighting, composites, and sensitivity testing.

What You'll Do

Ship scrapers and ingestion flows against messy, sometimes adversarial sources, using HTTP/2 clients, TLS-fingerprint evasion, and browser automation fallbacks, and keep them resilient as sources change

Own Postgres schema design and migrations end to end across per-country and per-domain schemas

Build and maintain medallion (bronze → silver → gold)



transforms that are idempotent, content-hashed, and lineage-tracked

Implement and defend index methodology: normalization, weighting, and composite construction where the math verifiably says what it claims (our scoring core is held to 100% test coverage)

Assess data feasibility early, clarify requirements with partners, and convert ambiguous index ideas into executable plans

Take an index end to end: sourcing, validation, methodology, publication, and refresh planning

Operate pipelines on our orchestration stack (Prefect dispatching per-flow ECS Fargate tasks) with observability everywhere

What We're Looking For

Our stack is deliberately modern (Python 3.14, uv, ruff, ty, polars, Prefect 3, marimo). We don't filter on those exact tools; we hire for Python and data depth and expect a short ramp.

Strong Python and SQL; you have designed Postgres schemas and owned migrations (SQLAlchemy and Alembic, or equivalents) in production

Data pipeline experience with a lakehouse/medallion mindset: idempotent ingestion, content hashing, and lineage are habits, not aspirations

Web scraping beyond requests: anti-bot evasion, browser automation, and resilience against messy or hostile sources

Statistics literacy for index methodology: winsorization, normalization, weighting, and sensitivity testing, and you can reason about whether an index's math supports its claims

Comfort with modern Python tooling and CI discipline: typing, linting, coverage gates,



and conventional commits

Product discovery instincts: you talk with partners in plain language, assess data feasibility before committing, and flag what is proven versus assumed

End-to-end ownership: you are a pragmatic generalist who moves across data, backend, infrastructure, and basic product decisions in an uncertain environment

Nice to Have

Prefect experience, or Airflow/Dagster with willingness to switch

AWS (ECS, S3) and Terraform polars, pyarrow, and marimo or a Jupyter background

LLM-in-pipeline experience (pydantic-ai, AWS Bedrock, evals)

Actuarial, quantitative research, or data science background in ranking or index construction

Experience with government open data (permits, energy, environmental, or economic datasets)

Comfort working alongside AI tooling; our repos are agent-forward (Claude agent teams, spec-driven docs)

What We Offer

High-impact work at the intersection of AI and critical infrastructure regulation

End-to-end ownership of indices, from raw source to published methodology

Small team with outsized influence; your feasibility calls shape what we build

Modern AI-native development environment (Claude Code, Cursor, multi-model orchestration)

Remote-first

Competitive compensation

Values We Hire For

Character: integrity and trustworthiness above all

Competency: evoking trust and reliably delivering

Togetherness: family-level support and alignment

Impact: meaningful outcomes over activity

Commitment: ownership and follow-through

Equal Opportunity Statement

We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law.

📌 Data Engineer, Red Tape Index - Labrynth (Argentina)
🏢 Infinity
📍 Argentina