What You’ll Do

• Strategic Solutioning: Develop compelling proposals, presentations, and statements of work (SOW) to drive business growth. Identify opportunities for Archetype in data and analytics, both in existing and new environments.

• Architectural Excellence: Lead the modern data architecture pillar, ensuring excellence in delivery, sales, people management, and operations.

• Subject Matter Expertise: As a senior leader, engage with clients, influence executives, and drive customer-focused solutions. Leverage your deep understanding of data and analytics to deliver value.

• Financial Insights: Conduct TCO (Total Cost of Ownership) and ROI (Return on Investment) analyses to guide technical decisions.

• Platform Design: Architect, design, and develop end-to-end data and analytic platforms (e.g., data warehouses, data lakes, ML models) using cloud services.

• Revenue Impact: Director hires will directly impact revenue up to $1M+, while Senior Director hires will have a responsibility of $2.5M+.

What You’ll Bring

• Experience: 10+ years in consulting leadership, with at least 5+ years specializing in data architecture and engineering solutions within cloud environments (Azure, AWS, GCP, Snowflake).

• Leadership: 4-8 years of team leadership, including technical pre-sales experience.

• Effective Communication: Proven ability to convey complex technical concepts to non-technical stakeholders.

• Business Development: Track record of growing existing client relationships and expanding new accounts.

• Cloud Expertise: Deep familiarity with cloud platform analytics services, including storage, permissions, private cloud, databases, virtual machines, and parallel processing technologies.

Required Experience

• 8+ years as a hands-on Solutions Architect designing and implementing data solutions.

• 4+ years previous Consulting leadership experience working with external customers with the ability to multitask, prioritize tasks, frequently change focus, and work across a variety of projects.

• Programming expertise in Java, Python, and/or Scala.

• Core cloud data platforms including Snowflake as well as AWS, Azure, Databricks, or GCP.

• SQL and the ability to write, debug, and optimize SQL queries.

• Demonstrated expertise in effectively leading and managing a team comprising Solution Architects and Data Engineers, fostering internal growth through coaching, mentoring, and performance management.

• Proven track record of collaborating with client stakeholders, technology partners, and cross-functional sales and delivery team members across distributed global teams, ensuring seamless, successful project delivery outcomes.

• Create strong cross-practice relationships to drive customer success.

• Exhibits a strong sense of ownership in resolving challenges, committed to ensuring exceptional outcomes for all aspects of project execution.

• Ability to develop end-to-end technical solutions into production — and to help ensure performance, security, scalability, and robust data integration.

• Client-facing written and verbal communication skills and experience.

• Create and deliver detailed presentations.

• Detailed solution documentation (e.g., including POCs and roadmaps, sequence diagrams, class hierarchies, logical system views, etc.).

• 4-year Bachelor's degree in Computer Science or a related field.

Preferred Qualifications (Nice-To-Have)

• Production experience in core data platforms: Snowflake, AWS, Azure, GCP, Hadoop, Databricks.

• Cloud and Distributed Data Storage: S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra, or other NoSQL storage systems.

• Data integration technologies: Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, NiFi, AWS Data Migration Services, Azure DataFactory, Informatica Intelligent Cloud Services (IICS), Google DataProc, or other data integration technologies.

• Multiple data sources: (e.g., queues, relational databases, files, search, API).

• Complete software development life cycle experience: including design, documentation, implementation, testing, and deployment.

• Automated data transformation and data curation: dbt, Spark, Spark streaming, automated pipelines.

• Workflow Management and Orchestration: Airflow, AWS Managed Airflow, Luigi, NiFi.

• Methodologies: Agile Project Management, Data Modeling (e.g., Kimball, Data Vault).

Most of the work can be managed remotely but may require occasional travel to client sites.