Data Engineer (Python, PySpark & Databricks)

Description: Design, build, and support scalable data pipelines using Python, Apache Spark, PySpark, and Databricks. Modernize and migrate legacy data processing workloads to secure, cloud-native platforms. Build and maintain batch data ingestion pipelines from structured and unstructured sources. Integrate data from REST APIs, SharePoint, document repositories, enterprise applications, and cloud platforms. Implement data quality, monitoring, observability, and operational controls. Optimize data workloads for performance, scalability, reliability, and cost efficiency. Develop document extraction, classification, metadata enrichment, and automation pipelines. Apply software engineering practices including Git, CI/CD, automated testing, and code reviews. Collaborate with architects, developers, tax subject matter experts, and platform teams. Requirements: 3+ years of experience in data engineering. Strong Python development experience. Strong Apache Spark and PySpark experience. Hands-on experience with Databricks. Strong SQL and data modelling skills. Experience building, testing, deploying, and troubleshooting production ETL/ELT pipelines. Experience with Git-based source control and CI/CD pipelines. Experience consuming REST APIs for data ingestion, including authentication, pagination, and failure handling. Strong understanding of data quality, reliability, and data engineering best practices. Benefits: A stable, long-term contract with opportunities for career growth. Private health insurance. A remote-friendly culture that promotes work-life balance. Continuous training, mentorship, and learning programs. Free access to AI training resources and state-of-the-art AI tools. Flexible Paid Time Off (PTO) policy and paid holiday days. Challenging, world-class software projects for clients in the US and LatAm.