Databricks Engineer

CGI•Lafayette, LA

1d•Hybrid

About The Position

CGI is seeking an experienced Databricks Engineer for its AI Cloud Engineering (ACE) Practice. The role delivers proofs of concept, pilots, and production solutions while supporting client engagements across the United States. It is based in Lafayette, Louisiana, and is described as a full-time, on-site opportunity in a hybrid model. The engineer designs, develops, and supports enterprise-scale data and analytics solutions on the Databricks Lakehouse Platform. The position involves building and optimizing data pipelines and ETL/ELT processes, implementing governance and data quality practices, modernizing legacy platforms, and enabling analytics, AI/ML, and reporting use cases. The role also contributes to client workshops and architecture reviews, reusable solutions and best practices, and mentoring junior engineers.

Requirements

At least 5 years of experience in Data Engineering, Big Data, Cloud Data Platforms, or Analytics Engineering.
At least 2 years of hands-on Databricks experience delivering enterprise data and analytics solutions.
Strong expertise with the Databricks Lakehouse Platform, Delta Lake, Apache Spark, Unity Catalog, and Databricks Workflows.
Proficiency in Python, SQL, and PySpark.
Experience with Azure, AWS, or GCP.
Experience building dashboards using Power BI, Databricks SQL Dashboards, Tableau, or equivalent BI tools.
Knowledge of Spark optimization, performance tuning, monitoring, CI/CD, and DevOps practices.
Strong consulting, communication, and customer-facing skills.
Commitment to staying current with Databricks releases, cloud technologies, analytics, AI, and industry best practices.
Bachelor's degree in computer science or a related field.

Nice To Haves

Databricks Data Engineer Associate/Professional or Databricks Solutions Architect certification.
Experience with Microsoft Fabric, Azure Data Factory, Synapse Analytics, or similar modern data platforms.
Experience with MLflow, MLOps, Feature Stores, and AI/ML lifecycle management.
Experience with Generative AI, LLMs, Vector Search, RAG architectures, and Model Serving.
Familiarity with Databricks Lakehouse Platform for Analytics (LTAP).
Understanding of Databricks Genie, conversational analytics, and AI-powered BI capabilities.
Understanding of Agent Bricks/Agentic AI patterns, agent orchestration frameworks, and enterprise AI implementations.
Experience with Structured Streaming and real-time data processing.
Enterprise data warehouse modernization and cloud migration experience.

Responsibilities

Design, develop, and support enterprise-scale data and analytics solutions using the Databricks Lakehouse Platform.
Collaborate with Enterprise Technology Practice leaders, solution architects, and client teams to deliver proofs of concept, pilots, and production implementations.
Build and maintain scalable data pipelines using Databricks, Delta Lake, Apache Spark, Unity Catalog, and Databricks Workflows.
Develop ETL/ELT processes for batch and streaming data integration.
Optimize Databricks and Spark workloads for performance, scalability, reliability, and cost efficiency.
Implement data governance, security, lineage, and data quality best practices.
Support migration and modernization initiatives from legacy data platforms to Databricks Lakehouse architectures.
Partner with analytics, AI/ML, and business stakeholders to enable MLflow, MLOps, GenAI, and LLM use cases.
Design and develop reporting and visualization solutions using Power BI, Databricks SQL Dashboards, Tableau, or similar platforms.
Participate in solution design workshops, client presentations, architecture reviews, and technical discovery sessions.
Contribute to reusable accelerators, reference architectures, best practices, and innovation initiatives.
Mentor junior engineers and support client engagements across the U.S. as needed.