Data Platform Engineer

Kestra Financial • Tempe, AZ

6d

About The Position

We are seeking a seasoned Databricks Data Engineer with expertise in Azure cloud services and the Databricks Lakehouse platform. The role involves designing and optimizing large-scale data pipelines, modernizing cloud-based data ecosystems, and enabling secure, governed data solutions. Strong skills in SQL, Python, PySpark, ETL/ELT frameworks, and experience with Delta Lake, Unity Catalog, and CI/CD automation are essential.

Requirements

• 8+ years of experience designing and developing scalable data pipelines in modern data warehousing environments, with full ownership of end-to-end delivery.
• Expertise in data engineering and data warehousing, consistently delivering enterprise-grade solutions.
• Proven ability to lead and coordinate data initiatives across cross-functional and matrixed organizations.
• Advanced proficiency in SQL, Python, and ETL/ELT frameworks, including performance tuning and optimization.
• Hands-on experience with Azure, Snowflake, and Databricks, and integration with enterprise systems.

Responsibilities

• Design, build, and optimize large-scale data pipelines on the Databricks Lakehouse platform, ensuring reliability, scalability, and governance.
• Modernize the Azure-based data ecosystem, contributing to cloud architecture, distributed data engineering, data modeling, security, and CI/CD automation.
• Utilize Apache Airflow and similar tools for orchestration and workflow automation.
• Work with financial or regulated datasets, applying strong compliance and governance practices.
• Develop and optimize ETL/ELT pipelines using Python, PySpark, Spark SQL, and Databricks notebooks.
• Design and optimize Delta Lake data models for reliability, performance, and scalability.
• Implement and manage Unity Catalog for RBAC, lineage, governance, and secure data sharing.
• Build reusable frameworks using Databricks Workflows, Repos, and Delta Live Tables.
• Create scalable ingestion pipelines for APIs, databases, files, streaming sources, and MDM systems.
• Automate API ingestion and workflows using Python and REST APIs.
• Support data governance, lineage, cataloging, and metadata initiatives.
• Enable downstream consumption for BI, data science, and application workloads.
• Write optimized SQL/T-SQL queries, stored procedures, and curated datasets for reporting.
• Automate deployments, DevOps workflows, testing pipelines, and workspace configuration.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees