Data Engineer
Builds and operates the pipelines that collect, move and expose data at scale: ingestion, warehouse, orchestration.
Why this hire matters.
Everything you want to do with data — analytics, ML, AI, customer reporting — rests on what the data engineer builds. Without one: ingestion cobbled together in fragile scripts, pipelines breaking overnight, data late or incomplete on the morning of board meetings. Every hour of data downtime propagates to every team that depends on it. A strong data engineer builds pipelines you stop thinking about, with monitoring, error recovery and controlled cost. In the AI era the role gains a new dimension: your models' quality will depend first on your data flows' quality. This is the foundation — hire it before stacking data scientists and AI projects on ground that doesn't exist.
Key missions.
- Design and operate ingestion pipelines (batch and streaming)
- Structure the data warehouse / lakehouse
- Orchestrate and monitor workflows (Airflow, Dagster)
- Guarantee data quality, freshness and lineage
- Optimize processing cost and performance
- Expose data to teams (analytics, ML, product)
- Industrialize with software practices (tests, CI/CD)
Skills.
Technical skills
- Advanced Python and SQL
- Orchestration (Airflow, Dagster)
- Warehouses (BigQuery, Snowflake, Databricks)
- Streaming (Kafka) where relevant
- IaC and cloud
Expected qualities
- Reliability
- Pragmatism
- Cross-team collaboration
Common stack
Salaries 2025-2026
| Level | Experience | Annual gross base |
|---|---|---|
| Junior | 0-2 yrs | 42–52 k€ |
| Mid-level | 2-5 yrs | 52–65 k€ |
| Senior | 5-8 yrs | 65–85 k€ |
| Lead | 8+ yrs | 85–110 k€ |
Paris market ranges, 2025-2026.
Outside the Paris region, expect 10 to 20 % less.
Sources : Silkhom 2026 (médiane 57 600 €) · Carrières.dev 2026 (médiane 78 000 €, bulletins vérifiés) · Lity 2026 · francesalaires 2025
Hiring this profile.
Typical background
Data-oriented software engineer or a data profile who leveled up into engineering. Software engineering skills separate them from 'script' profiles.
When to hire
As soon as data becomes a serious product or decision asset — often Series A. Always before scaling a data science team.
Career path
Senior → Lead Data Engineering → Data Architect or Head of Data Platform → Chief Data Officer.