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