Senior Data Engineer, Python, Databricks, Spark

Responsibilities

• Design and implement scalable data architectures
• Develop robust ETL pipelines
• Support advanced analytics initiatives across multiple cloud platforms
• Collaborate closely with engineering leadership, stakeholders, and cross-functional teams
• Contribute to architectural decisions, engineering best practices, and continuous improvement initiatives
• Work on modern cloud-native data platforms supporting both real-time and batch processing workloads
• Design end-to-end data pipelines
• Build optimized OLAP systems
• Enable scalable analytics platforms for complex business domains

Requirements

• Minimum 5 years of professional experience as a Data Engineer
• Strong hands‑on experience with Python
• Advanced SQL skills for large‑scale data processing
• Strong experience with Databricks and Apache Spark
• Experience designing and maintaining ETL pipelines using dbt
• Experience with Apache Airflow for orchestration and workflow automation
• Hands‑on experience with cloud platforms including AWS, GCP, or Azure
• Experience with AWS S3, Google BigQuery, Google Cloud Storage, or Azure Databricks
• Knowledge of Infrastructure as Code (IaC) using Terraform
• Experience with event‑driven or streaming technologies such as Apache Kafka
• Experience with Docker and Kubernetes
• Strong understanding of dimensional data modeling methodologies including Kimball
• Experience designing OLAP systems, Star Schemas, and Semantic Models
• Understanding of data architecture methodologies such as Inmon and Data Vault
• Experience with CI/CD workflows and Git version control
• Strong communication and collaboration skills
• Ability to work closely with technical and non-technical stakeholders
• Fluency in English

Tools & Technologies

• AWS S3
• Google BigQuery
• Google Cloud Storage
• Azure Databricks
• Terraform
• Apache Kafka
• Docker
• Kubernetes
• CI/CD
• Git