Description

Responsibilities  

• Design, develop, test, deploy, and maintain enterprise data engineering solutions using modern cloud and big data technologies.
• Design and implement scalable ETL/ELT pipelines utilizing **Databricks, Apache Spark (PySpark), Delta Lake, and Azure Data Factory**.
• Build high-performance data ingestion, transformation, and integration frameworks supporting enterprise analytics and reporting.
• Develop, optimize, and maintain complex SQL queries, stored procedures, and data transformation processes.
• Design and implement scalable data models supporting business intelligence, analytics, and AI initiatives.
• Build and integrate RESTful APIs, event-driven architectures, and legacy SOAP services to facilitate seamless enterprise data exchange.
• Develop and maintain streaming and messaging solutions using **Apache Kafka**.
• Monitor, troubleshoot, and optimize production data pipelines while performing root cause analysis and implementing long-term solutions.
• Implement data quality, governance, security, and performance best practices across enterprise platforms.
• Manage source code using Git while following CI/CD and enterprise DevOps best practices.
• Collaborate with Solution Architects, Product Owners, Business Analysts, and cross-functional engineering teams to translate business requirements into technical solutions.
• Participate in Agile ceremonies including sprint planning, backlog refinement, architecture discussions, code reviews, and retrospectives.
• Create technical documentation, deployment artifacts, testing documentation, and operational runbooks.
• Mentor junior engineers and contribute to engineering standards, best practices, and continuous improvement initiatives.

Required qualifications to be successful in this role

We're looking for an experienced engineer who enjoys solving complex technical challenges while delivering modern cloud-based data solutions.

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical discipline (or equivalent professional experience)

6+ years** of experience designing, developing, and supporting enterprise data platforms, cloud analytics solutions, or large-scale data engineering initiatives

Strong hands-on experience with:

• Databricks
• Apache Spark (PySpark)
• Python
• SQL
• Scala

Experience working with the Databricks ecosystem, including:

• Databricks Workspaces
• Delta Lake
• Unity Catalog
• Delta Live Tables (DLT)
• Databricks SQL
• MLflow
• Databricks Jobs
• Strong SQL development experience, including query optimization and performance tuning
• Experience designing and implementing ETL/ELT solutions using Azure Data Factory or comparable cloud integration platforms
• Experience with Apache Kafka or other event streaming technologies
• Experience integrating enterprise applications using REST APIs, JSON, XML, and SOAP web services
• Experience with Azure Data Lake Storage (ADLS Gen2) or comparable cloud storage platforms
• Experience using Git for source code management and collaborative software development
• Experience working within Linux environments, including shell scripting and command-line utilities
• Familiarity with Infrastructure as Code (IaC) tools such as Terraform is preferred
• Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, or similar DevOps platforms is preferred
• Strong analytical, troubleshooting, and problem-solving skills
• Experience working in Agile environments utilizing Scrum, Kanban, or SAFe methodologies
• Demonstrated ability to manage multiple priorities while delivering high-quality solutions
• Proven ability to work independently while mentoring teammates and contributing to technical leadership

Key Skills

Education

Bachelor's or Master's degrees