Data Engineer

At Slide, you will not only be part of a successful team, but you will also be a part of our Slide Vibe/award winning culture where collaboration and innovation are expected, recognized and awarded!

As an Azure Databricks Data Engineer at Slide, you will build and support data pipelines using Azure Data Factory, Databricks, PySpark, and Spark SQL. You'll partner with engineering and business teams to deliver reliable data for reporting, analytics, and operational decision-making.

Build Data Pipelines

• →Build and maintain ETL/ELT pipelines using Azure Data Factory and Databricks.
• →Develop PySpark and Spark SQL workloads to ingest, transform, and publish enterprise data.
• →Implement Delta Lake tables using a layered lakehouse architecture.

Optimize and Scale Data Solutions

• →Design scalable data models and processing frameworks for batch and incremental workloads.
• →Optimize Spark workloads, SQL queries, and storage solutions for performance and cost efficiency.
• →Implement data quality, monitoring, and observability practices across the platform.

Support Data Operations

• →Monitor, troubleshoot, and support production data pipelines.
• →Partner with analytics and business teams to deliver trusted, analytics-ready datasets.
• →Contribute to data engineering standards, governance, and platform improvements.

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or equivalent experience
• 3+ years of Data Engineering experience in the Property & Casualty (P&C) insurance industry.
• Hands-on experience building and supporting ETL/ELT pipelines using Azure Data Factory and Databricks.
• Strong SQL skills, including writing and optimizing queries against large datasets.
• Production experience with PySpark, Spark SQL, Delta Lake, and Databricks Lakehouse architectures.
• Experience implementing and supporting scalable cloud-based data solutions in Azure.
• Alignment with Slide’s Core Values and collaborative culture