Databricks Engineer
Employment Info
JOB DETAILS
Requirements
- Solid hands-on experience with the Databricks Lakehouse Platform, including:
- Delta Lake and practical use of Delta Live Tables (DLT) for pipeline development
- Unity Catalog for data governance, access control, and data organization
- Workflows and job orchestration for production-grade pipelines
- SQL Warehouses for analytics and performance-optimized querying
- Working exposure to advanced capabilities such as AI/BI features and agent-based workflows, with ability to apply them in client scenarios
- Proven experience designing and implementing ETL/ELT pipelines across batch and streaming workloads
- Strong understanding of data modelling techniques, data quality frameworks, and end-to-end pipeline lifecycle management
- Ability to apply modern data architecture patterns, including data products and domain-oriented data design
- Strong proficiency in Python and SQL for building scalable data pipelines
- Hands-on experience with PySpark (or equivalent) for distributed data processing
- Experience working with both batch and streaming pipelines, including performance optimization
- Applies coding best practices, testing, debugging, and code modularity in production environments
- Practical experience working with at least one cloud platform (Azure, AWS, or GCP)
- Hands-on exposure to cloud-native data services (e.g. storage layers, orchestration tools, ingestion services)
- Familiarity with APIs and integration patterns for connecting enterprise data systems
- Working experience with version control (Git) and CI/CD pipelines in data engineering contexts
- Applied understanding of DataOps and DevOps practices for reliable and repeatable deployments
- Experience contributing to deployment automation and release management in Lakehouse environments
- Ability to work independently on defined tasks while collaborating effectively with architects and senior engineers
- Strong communication skills with experience engaging both technical and business stakeholders
- Demonstrates ownership of deliverables and contributes to problem-solving in client engagements
- Proactive in continuous learning, certifications, and staying current with Databricks and cloud ecosystem advancements.
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