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Senior Data Architect

Position Description

We're growing rapidly and are looking for a senior-level Data Architect to help our clients design and build modern, large-scale cloud data platforms. If you enjoy solving complex data challenges, designing scalable architectures, and building data solutions across multiple cloud and technology platforms, CGI is the place for you.

The Senior Data Architect will be responsible for designing and delivering enterprise-grade data architectures, with Databricks serving as a key area of expertise. This role will lead end-to-end solution design-from data ingestion, integration, and modeling through performance optimization, governance, reliability, and cost management-while enabling analytics, AI/ML, and self-service data capabilities at scale.

We are looking for someone with deep Databricks experience as well as a strong foundation in modern data architecture and engineering. Successful candidates will be comfortable applying architectural principles across technologies and adapting their experience to platforms such as Google BigQuery, Snowflake, Microsoft Fabric, Azure data services, and other modern cloud data ecosystems.

This role offers a blend of hands-on engineering, technical leadership, architecture, and client consulting. You will help clients define their data strategy, make sound technology and architecture decisions, and implement scalable solutions that create measurable business value. You will also mentor engineering teams, contribute to CGI's data capabilities, and help shape solutions and proposals for new and existing clients.

Growth at CGI is driven by your goals, so if you're looking for an inclusive place where you're empowered to chart your own path, then we'd love to meet you.

This role is open to candidates in St. Louis, MO, Minneapolis, MN, or Des Moines, IA only, who are willing to work on-site in a hybrid model.

Your future duties and responsibilities

- Provide technical and architectural guidance on Databricks, modern data platforms, data engineering, and cloud-native solutions to clients and internal teams.

- Design scalable enterprise data architectures spanning data ingestion, transformation, storage, modeling, governance, analytics, and AI/ML workloads.

- Design and implement Lakehouse architectures using Databricks, including technologies such as Delta Lake, Apache Spark, Unity Catalog, Delta Live Tables/Lakeflow, and open table formats such as Apache Iceberg.

- Apply modern data architecture patterns across platforms such as Databricks, Google BigQuery, Snowflake, Microsoft Fabric, and native Azure, AWS, and GCP data services.

- Evaluate technology and architecture options based on client requirements rather than relying on a single technology or platform.

- Design and implement scalable batch and streaming data pipelines integrating data from multiple enterprise source systems.

- Lead and mentor data engineering teams, establishing engineering standards and best practices for data quality, performance, security, reliability, testing, and maintainability.

- Provide hands-on technical leadership in designing, developing, troubleshooting, and optimizing critical components of enterprise data platforms.

- Partner with client technology and business leaders to understand their challenges and translate business requirements into pragmatic data strategies and technical solutions.

- Guide data modernization and migration initiatives, including movement from legacy databases, data warehouses, and ETL platforms to modern cloud architectures.

- Deepen CGI's presence in the market by contributing to solution development, proposals, SOWs, client presentations, and strategies to grow our footprint within existing and new accounts.

Required qualifications to be successful in this role

- 10+ years of experience in data engineering, data architecture, data warehousing, or related disciplines, with demonstrated progression into architecture and technical leadership responsibilities.

- Significant hands-on experience designing and delivering enterprise solutions using Databricks, including Apache Spark, Databricks SQL, Delta Lake/Lakehouse architecture, Unity Catalog, and Python.

- Broad experience with modern data architecture concepts, including data lakes, data warehouses, Lakehouse architectures, distributed data processing, data integration, and analytical data platforms.

- Experience with at least one additional enterprise data platform or ecosystem beyond Databricks, such as Google BigQuery, Snowflake, Microsoft Fabric, Azure Synapse, or comparable technologies.

- Strong cloud architecture experience with Azure, AWS, and/or GCP and an understanding of how data architecture patterns translate across cloud providers.

- Data Engineering & ETL/ELT - Ability to design and implement scalable batch and/or streaming data pipelines using technologies such as Spark, SQL, Python, and cloud-native data integration services.

- Data Modeling - Expertise designing logical and physical data models for analytical, operational, and reporting use cases.

- Data Governance & Security - Experience with data catalogs, metadata management, lineage, role-based access control, data quality, and enterprise security and compliance requirements.

- Performance & Cost Optimization - Ability to diagnose and optimize data processing, storage, compute, and query performance while balancing scalability and cost.

- Hands-on Development - Ability and willingness to code, prototype solutions, conduct technical reviews, and troubleshoot critical issues when necessary.

- Architecture & Technology Selection - Ability to evaluate competing technologies, articulate tradeoffs, and recommend solutions based on business and technical requirements.

- Demonstrated Delivery Experience - Proven track record successfully delivering multiple complex, enterprise-scale data initiatives from architecture and design through implementation and production operation.

- Client Consulting - Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders and influence architecture decisions.

Desired skills

- Databricks Data Engineer Professional or related Databricks certification.

- Experience with Google BigQuery, Snowflake, Microsoft Fabric, Azure Data Factory, or other modern cloud data platforms and services.

- Experience modernizing legacy data warehouses, databases, and ETL workloads onto cloud-native platforms.

- Experience with DevOps and DataOps practices including CI/CD, infrastructure as code, automated testing, observability, and Agile delivery.

- Experience with metadata management, data cataloging, data lineage, data quality, and data mesh/domain-oriented architecture.

- Experience with open data formats and interoperability patterns, including Delta Lake and Apache Iceberg.

- Exposure to Databricks migration tooling such as Lakebridge and strategies for cross-cloud, cross-tenant, or cross-region data platform migrations.

- Experience designing data architectures supporting advanced analytics, machine learning, generative AI, and enter...

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