Overview

CVs must be submitted in English to be considered

Responsibilities

• Design, build, and maintain batch and streaming pipelines in Python, including the orchestration, scheduling, and monitoring around them.
• Write reusable, well-typed Python libraries and internal packages that other engineers and analysts build on.
• Build Python services and APIs that expose data to downstream applications, and integrate with third-party and internal APIs.
• Write unit, integration, and data-contract tests, and keep pipelines covered by automated CI.
• Profile and optimize Python code and data processing jobs for runtime, memory, and cost.
• Deploy and operate code in the cloud using containers, infrastructure as code, and CI/CD.
• Enforce access controls, secrets handling, and sensitive-data protections at every stage of the data lifecycle.
• Find and fix efficiency, reliability, cost, and correctness problems in existing code and pipelines, and refactor toward simpler designs.
• Replace one-off scripts and notebooks with tested, packaged, scheduled code.
• Implement data quality checks, validation, and monitoring that catch issues before consumers do.

Qualifications

• 2-4+ years of experience in the following areas:
• Shipping production Python, not just scripts or notebooks.
• Building and operating ETL/ELT pipelines at scale.
• Python data tooling: pandas, Polars, PyArrow, DuckDB, or PySpark.
• Schema enforcement and validation: Pydantic or similar.
• Workflow orchestration in code: Airflow, Dagster, Prefect, dbt, or Temporal.
• Testing and quality tooling: pytest, pyright, ty, pyrefly, ruff or black.
• Dependency and environment management: uv, Poetry, pip-tools, or conda.
• At least one public cloud (AWS, Azure, or GCP) and its Python SDK, with familiarity with the others.
• Cloud data platforms: Snowflake, Databricks, BigQuery, or Redshift.
• Relational and non-relational databases, including schema design.

Education

• Bachelor's degree or an equivalent combination of education, training and experience.
• Strong, idiomatic Python: data structures, typing, error handling, generators/iterators, context managers, and the standard library.
• Software engineering fundamentals: modular design, dependency management, packaging, semantic versioning, and API design.
• Testing discipline: pytest, fixtures, mocking, and writing code that is testable by construction.
• Debugging and profiling; able to reason about performance, concurrency, and memory in Python.
• Working knowledge of SQL, data modeling, governance, and privacy practices sufficient to design sound schemas and query them effectively.
• Able to explain technical trade-offs to both technical and business audiences.
• Python web/API frameworks (FastAPI, Flask).
• Async Python, multiprocessing, or other concurrency patterns.
• Streaming: Kafka, Kinesis, or Spark Structured Streaming.
• Infrastructure as code and automation (Terraform, GitHub Actions).
• Docker and Kubernetes.
• A second language: Go, Rust, Scala, or TypeScript.
• Generative AI, machine learning, or data science tooling.
• Knowledge, Skills & Abilities