Sr. Databricks Developer

What youll do:

• Architect, design, and implement scalable and performant Databricks-based data pipelines, including batch and streaming workloads.
• Optimize Databricks Lakehouse architecture - including Delta Lake, Unity Catalog, and Photon - for cost efficiency, data reliability, and query performance at scale.
• Define and implement advanced data models and ontologies to support complex analytics and AI/ML workflows.
• Design and tune complex Spark applications (PySpark and/or Scala) for high-volume data processing, including structured streaming and incremental processing.
• Implement orchestration workflows (e.g., Databricks Workflows, DLT, or similar) to automate data pipelines with robust monitoring and alerting.
• Drive best practices for data governance, security, and lineage, leveraging Unity Catalog, table ACLs, and audit capabilities.
• Optimize cluster configurations, autoscaling strategies, and cost management within Databricks environments.
• Collaborate with data scientists and analytics engineers to enable self-service access to high-quality, well-documented datasets and features.
• Lead complex troubleshooting efforts, deep performance tuning, and root-cause analysis across distributed systems.
• Author and maintain technical artifacts, including architecture diagrams, design docs, reference implementations, and best practice guides.
• Mentor and coach engineers across teams on Databricks internals, Spark performance tuning, and modern data engineering architectures.
• Stay on top of emerging Databricks features (e.g., Mosaic AI, serverless compute, Delta Live Tables), advocating for their adoption when they bring tangible value.

Youll be expected to have:

• Bachelor s or higher degree in Computer Science, Software Engineering, or a related field.
• 7+ years of software engineering experience, with 5+ years specifically in building large-scale data platforms.
• Expert-level proficiency in Databricks, including:
• Delta Lake internals (ACID transactions, time travel, Z-ordering, data compaction strategies)
• Unity Catalog for data governance, security, and lineage
• Advanced Spark tuning (shuffle management, partitioning strategies, caching, cluster configuration)
• Databricks SQL Warehousing, Workflows, and MLflow integration
• Strong expertise with cloud-native data engineering in Azure or AWS, including storage (ADLS/S3), networking, and IAM integration.
• Knowledge of streaming architectures (Apache Kafka, structured streaming), schema evolution, and event-driven data modeling.
• Hands-on experience with metadata catalogs, orchestration frameworks, and observability tooling (e.g., Datadog, Grafana).
• Strong understanding of distributed systems, fault tolerance, and large-scale performance engineering.
• Proven ability to solve complex, ambiguous problems and make architectural trade-offs in real-world production systems.
• Excellent technical communication skills - ability to document, present, and explain technical concepts clearly to varied audiences.
• Prior experience mentoring engineers, conducting design reviews, and setting technical direction across teams.
• A continuous learner mindset - actively keeping pace with evolving Databricks platform capabilities and broader data engineering trends.

Preferred but not required:

• Experience with Power BI Direct Lake mode and semantic modelling for Lakehouse-based analytics.
• Familiarity with Mosaic AI, Databricks GenAI capabilities, or Feature Store implementations.
• Contributions to open-source Spark, Databricks community content, or certifications (e.g., Databricks Certified Data Engineer Professional).

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.