Senior Data Engineer (Databricks & Cloud Analytics)

CGI•Salt Lake City, UT

1d•Onsite

About The Position

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.

Requirements

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
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

Nice To Haves

Familiarity with Infrastructure as Code (IaC) tools such as Terraform is preferred
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

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.