Senior Data Pipelines Engineer: Lakehouse & Quality

amo; Paris; On-site; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Central Paris office near Opera

Health care 100% coverage

8-9 weeks vacation per year

Onsite five days a week

Job summary

amo is seeking a senior data engineer in Paris to build and maintain batch and streaming data pipelines, moving production data into archival storage, and improving the quality and observability of derived datasets.

You will design validations for freshness and schema correctness, create reusable datasets, and optimize queries for speed and cost. The role entails hands-on work with PySpark, Spark SQL, Parquet, and Iceberg in a central Paris office.

Qualifications

• Senior-level experience building and operating data pipelines.
• Strong Python and SQL proficiency with production experience in Python/Scala.
• Hands-on with PySpark, Spark SQL, Spark Streaming, Parquet, Iceberg.
• In-depth understanding of batch and stream processing and query optimization.
• Familiarity with distributed data systems and data lake architectures.

Responsibilities

• Build and maintain batch and streaming data pipelines.
• Move production data into archival/data lake storage safely and observably.
• Improve quality, reliability, and observability of derived datasets.
• Design validations for freshness, completeness, schema correctness, and data quality.
• Build reusable datasets to reduce one-off data extraction and aggregation work.
• Optimize queries and data processing for correctness, latency, and cost.
• Prototype with the right tools, then integrate production experiments into amo’s systems.

Skills

Senior data pipelines

Python

SQL

Production Python/Scala

PySpark / Spark

Parquet / Iceberg

Batch & streaming

Query optimization

Distributed data systems

Data lakes / storage formats

Job description

amo is seeking a senior data engineer in Paris to build and maintain batch and streaming data pipelines, moving production data into archival storage, and improving the quality and observability of derived datasets.

You will design validations for freshness and schema correctness, create reusable datasets, and optimize queries for speed and cost. The role entails hands-on work with PySpark, Spark SQL, Parquet, and Iceberg in a central Paris office.