Senior Data Engineer (Search) (m/f/x) at Cortea AI

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

22 Aug, 26

Salary

0.0

Posted On

24 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, BigQuery, LLM Evaluation, Data Pipeline Construction, RAG, Document Extraction, OCR, Distributed Processing, Batch Processing, Streaming, Observability, Cloud Data Warehousing, Temporal, GCP, Data Infrastructure

Industry

Software Development

Description

About us We’re Cortea, a Berlin-based startup rebuilding how audits are done. Audits are still driven by spreadsheets, checklists, and manual reviews in one of the most regulated, high-stakes areas of business. We build AI tools for auditors, so highly educated auditors no longer spend 60+ hour weeks on manual work and can focus on judgment, trust, and choices that matter. With €10m+ in funding, a live product, and paying customers across Europe, we’re at a stage where your choices will directly shape Cortea’s future, and how audits are done. Your Role You'll own the data and quality infrastructure that makes Cortea's AI pipeline trustworthy and continuously improving. Pipeline data, evals, observability, ground truth, retrieval quality. You're a builder. You write production Python. You think in pipelines and feedback loops, not notebooks. You've worked with LLM outputs in production and have strong opinions about how to make probabilistic systems measurable. What you’ll do Build and operate the data pipelines behind Cortea's AI. Every model call, every pipeline state, every customer document, captured, queryable, observable Create the foundation for evaluating agent performance and quality. Make probabilistic quality measurable, regression-detectable, and reproducible across model versions Maintain observability of agent cost and optimizations Improve document extraction and retrieval quality on the documents that matter most (financial statements, audit reports, complex tables) Maintain the Data FBigQuery foundation engineers, PMs, and founders use to make decisions Partner with engineering and product to turn customer feedback into measurable, shipped improvements Success at 6 months Eval framework live across our core pipelines — every ship is measured before it goes out Cost and quality observability on every pipeline run, alerting that catches regressions early Document extraction and retrieval quality measurably better on the documents customers care about most Trusted by engineers and founders to own the data foundation end-to-end Qualifications 4+ years total, 3+ shipping production data infrastructure (pipelines, warehouses, observability) Strong Python and SQL. Reads code to understand data, doesn't just trust schemas Has worked with LLM outputs in production. Has built or seriously used an eval framework Comfortable with cloud data warehouses (BigQuery preferred, Snowflake/Redshift fine), distributed processing, batch and streaming Cares about outcomes over process, clarity over frameworks Comfortable with startup environment high autonomy, high ambiguity, high speed Bonus Built or seriously contributed to retrieval/RAG, document extraction, or OCR systems GCP / BigQuery / Temporal experience Background in audit, compliance, legal, or another document-heavy professional services domain Speaks German No one checks every box. If you’ve shipped retrieval systems and like owning evaluations and pipelines, let’s talk. What we offer Attractive compensation: competitive salary plus significant equity High impact & growth: Shape AI at a scaling startup Personal development: Learning budget for courses and conferences Startup perks: Flexible vacation, team lunches, retreats, central Berlin office Interview process First Call — Intro to Cortea with our Founders Associate Leon Second Call — Technical interview with with a member of our technical staff Third Call — Deep dive into our culture with our Co-Founder Philipp On-site Half-Day (Berlin) — Meet the team and work on a real problem together We’re an equal-opportunity team and encourage women and underrepresented groups to apply.

Responsibilities

Own the data and quality infrastructure to make the AI pipeline trustworthy, focusing on observability, retrieval quality, and evaluation frameworks. Build and operate production data pipelines to capture model calls and customer documents for continuous improvement.