Mid-Level Data Engineer (Remote, Contract) - Airflow | Spark | Python | SQL

Tasks and Responsibilities

We are a fast-growing Data Science and Data Engineering consultancy, and we are expanding our team to deliver large, complex, high-impact projects.

Here, AI is not just a “concept”: it is part of the daily work in automations, pipelines, observability, quality, and productivity, always with a focus on robustness and scale for enterprise clients.

If you enjoy building things that actually work — with data arriving on time, reliable orchestration, governance, performance, and traceability — come with us. 👇

✅ What you will do

• Build and evolve data pipelines (batch and, when necessary, near real-time)
• Orchestrate workflows with Apache Airflow: resilient, idempotent, and easy-to-maintain DAGs, with well-handled dependencies, retries, and backfills
• Develop and optimize distributed processing with Spark (PySpark) in environments such as Databricks
• Work with Python and SQL for ingestion, transformation, and validation
• Implement data quality, monitoring/alerts, logs, and observability
• Collaborate with Data Science, Analytics, and Product teams
• Document decisions and support engineering standards (Git, CI/CD, code review)

🎯 Requirements

• Hands-on experience with Apache Airflow in production: creating and maintaining DAGs, operators, sensors, and hooks, scheduling, retries, backfill, and troubleshooting executions
• Solid Spark / PySpark (transformations, optimization, efficient read/write)
• Python applied to data engineering (ingestion, API integration, transformation, and testing)
• Strong SQL (joins, CTEs, window functions, modeling, and performance)
• Clear understanding of data modeling and governance
• Plus: Databricks (Jobs, notebooks, clusters, Delta Lake/medallion architecture, Unity Catalog), managed Airflow or Airflow on Kubernetes (MWAA, Cloud Composer, Astronomer), cloud (Azure, AWS, or GCP), dbt, and streaming (Kafka, Spark Structured Streaming)

💡 What you will find here

• Challenging projects with large companies and real data
• A fast-growing environment with room for ownership and growth
• Team culture: collaboration, autonomy, delivery, and quality
• Modern stack, with real encouragement for automation and best practices