Senior Data Engineering

A senior Data Engineer responsible for building and evolving the data infrastructure of a large-scale insurance platform, ensuring reliable, scalable pipelines and data models, and collaborating with business teams to enable data-driven decisions.

Location: Remote

Responsibilities

• Design, implement, and maintain robust and scalable data pipelines integrating data from APIs, internal systems, and external sources.
• Develop and evolve data models in Data Lakehouse (Delta Lake), structuring layers for complex analysis and platform growth.
• Configure and operate orchestration tools (Airflow, Dagster or similar) to ensure process quality and timeliness.
• Contribute to building a unified data dictionary, apply CI/CD practices and version control to pipelines, and ensure data integrity and traceability.
• Collaborate with product teams, analysts, and data scientists to translate business questions into concrete analyses and identify improvement opportunities.
• Support data initiatives involving Machine Learning and predictive models to anticipate trends and optimize operations.

Requirements

• Proven experience in Data Engineering, with a track record of building and maintaining robust, scalable pipelines in high-volume environments.
• Experience with Databricks and/or other distributed analytical platforms, including Data Lakehouse and Delta Lake architectures.
• Advanced proficiency in SQL and familiarity with relational and non-relational databases (e.g., PostgreSQL, MongoDB, Cassandra).
• Expertise in Python with PySpark and Big Data frameworks like Apache Spark or Apache Flink.
• Experience with pipeline orchestration tools such as Apache Airflow or Dagster.
• Experience with cloud environments (Azure, AWS, GCP) and distributed storage services (Azure Data Lake Storage, AWS S3, GCS).
• Knowledge of CI/CD practices and version control with Git/GitHub applied to data pipelines.
• Familiarity with real-time data ingestion via Kafka, Kinesis or similar.
• Practical use of AI in work, such as using LLMs for SQL transformation, pipeline documentation automation, anomaly detection, or query optimization.
• Alignment with company values: Connect genuinely, Embrace challenges, Deliver with pride.