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 #J-18808-Ljbffr
