16283 - Senior Database Engineer (Databricks & Cloud Analytics)

Position Description

Are you passionate about building modern data platforms that enable organizations to make smarter, faster decisions? Do you thrive on solving complex engineering challenges using cloud technologies, big data frameworks, and modern analytics platforms?

CGI is seeking an experienced **Senior Data Engineer (Databricks & Cloud Analytics)** to join our growing team in **Salt Lake City, UT**. In this role, you will partner with solution architects, business analysts, data scientists, and client stakeholders to design, develop, and support enterprise-scale data solutions that power mission-critical business initiatives for commercial and public sector clients.

As a trusted technical consultant, you'll leverage technologies including **Databricks, Apache Spark (PySpark), Azure Data Factory, Azure Data Lake, Kafka, Python, SQL, Scala, and cloud-native services** to build high-performance data pipelines, modern integrations, and scalable analytics platforms.

This is an excellent opportunity for an engineer who enjoys combining technical expertise with business problem-solving while working in a collaborative Agile environment focused on innovation, quality, and continuous improvement.

This position is based onsite at a client location in the **Salt Lake City, UT** area.

If you're passionate about designing scalable cloud data solutions, solving complex engineering challenges, and delivering innovative analytics capabilities that drive business outcomes, we encourage you to apply and join CGI's Data & Analytics team.

Your future duties and responsibilities

How you'll make an impact

As a Senior Data Engineer, you will play a critical role in designing, implementing, and supporting secure, scalable, and high-performing enterprise data platforms.

Responsibilities include:

• 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

Desired Skills:

The ideal candidate will also possess experience with one or more of the following:

• Microsoft Azure cloud services
• Azure Synapse Analytics
• Microsoft Fabric
• Power BI
• Data governance and metadata management
• DataOps and MLOps practices
• Enterprise data warehousing
• Master Data Management (MDM)
• Financial services or public sector data environments

Communication & Collaboration

Successful candidates will demonstrate the ability to:

• Clearly communicate complex technical concepts to both technical and non-technical audiences.
• Collaborate effectively with architects, developers, business analysts, product owners, and client stakeholders.
• Build trusted relationships across cross-functional teams while delivering innovative, high-quality solutions.
• Foster a culture of knowledge sharing, continuous learning, and engineering excellence.