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

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

Senior Data Engineer, Python, Databricks, Spark

Key skills