Data Engineer

Location Type: Hybrid

Design, build, and optimize scalable data pipelines using the Databricks Lakehouse platform, ensuring reliability, performance, and governance
Contribute to the modernization of cloud-based data ecosystems, including architecture, modeling, security, and CI/CD automation
Develop and maintain ETL/ELT pipelines using Python, PySpark, Spark SQL, and notebook-based workflows
Orchestrate workflows using tools such as Apache Airflow or similar platforms
Design and optimize Delta Lake data models for performance and scalability
Implement and manage data governance solutions, including role-based access control (RBAC), data lineage, and secure data sharing
Build reusable frameworks and workflows using Databricks tools such as Workflows, Repos, and Delta Live Tables
Develop scalable data ingestion pipelines from APIs, databases, files, streaming platforms, and other enterprise data sources
Automate API ingestion and data workflows using Python and REST APIs
Support enterprise data governance, metadata management, and cataloging initiatives
Enable downstream data consumption for business intelligence, analytics, data science, and applications
Write and optimize complex SQL queries, stored procedures, and curated datasets for reporting and analytics
Support DevOps practices including automated deployments, testing, and environment configuration

8+ years of experience designing and developing scalable data pipelines in modern data environments
Strong expertise in data engineering, data warehousing, and distributed data processing
Proven ability to lead and collaborate across cross-functional teams
Advanced proficiency in SQL and Python, including performance tuning and optimization
Hands-on experience with Azure cloud services, Databricks, and modern data platforms
Experience integrating data solutions with enterprise systems and tools