Data Architect – Delivery

Role Overview

• Own end-to-end delivery of data platform engagements: managing scope, timeline, budget, team coordination, and client satisfaction from kickoff through handoff
• Lead cross-functional delivery teams that may include project managers, business analysts, data engineers, developers, and change management professionals — ensuring the right skills are in place at each stage
• Manage engagement health proactively: identify delivery risks early, escalate appropriately, and maintain clear communication with client stakeholders throughout
• Serve as the primary client relationship owner during delivery, building trust through reliable execution and transparent progress reporting
• Conduct technical requirements gathering and capability assessments to establish a sound foundation for each engagement
• Design and implement modern cloud data architectures across AWS, Azure, and Google Cloud — including data lakes, lakehouses, data warehouses, and real-time streaming platforms
• Lead migration of legacy on-premise data systems to cloud-native architectures, ensuring performance, scalability, and cost-efficiency
• Build and oversee ETL and data pipelines using cloud-native automation and orchestration tools
• Design and implement machine learning infrastructure on cloud platforms — including feature stores, model training pipelines, experiment tracking, and model serving and monitoring in production
• Architect data foundations that support downstream ML, AI, and agentic workflows, including intelligent document processing, knowledge retrieval, and structured/unstructured data integration
• Establish and enforce data quality, governance, and observability standards across client environments, including ML model performance monitoring and drift detection
• Evaluate and recommend appropriate tooling across the modern data and ML stack based on client context, capability, and long-term roadmap
• Partner with sales teams as a technical authority during discovery calls, client workshops, and solution presentations
• Develop proposals including solution architecture, scope of work, resource requirements, and budgetary estimates

Requirements

• Bachelor’s degree in a related discipline and 8 years’ experience in a related field OR a Master’s degree and 6 years’ experience OR a Ph.D. and 3 years of experience OR 12 years’ experience in a related field
• 1+ years of hands-on experience building and deploying machine learning solutions in a cloud environment — including model training pipelines, feature engineering, and production model serving using platforms such as Google Vertex AI, Amazon SageMaker, or Azure Machine Learning
• 3+ years building and maintaining ETL and data pipelines using cloud-native automation and orchestration tools
• Hands-on experience with cloud data warehousing and lakehouse platforms (e.g., Google BigQuery, Amazon Redshift, Azure Synapse Analytics, Microsoft Fabric, Databricks, Snowflake)
• Proficiency in Python for data engineering tasks; working knowledge of at least one additional language relevant to the data stack (e.g., SQL at scale, Scala, Java)
• Strong communication skills — able to present technical architectures to executive audiences and document solutions clearly for both technical and non-technical stakeholders.

Tech Stack

• Amazon Redshift
• AWS
• Azure
• BigQuery
• Cloud
• ETL
• Java
• Python
• Scala
• SQL

Benefits

• The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties
• seven paid holidays throughout the calendar year
• up to 160 hours of paid wellness annually for their own wellness or that of family members
• additional paid time off in the form of bereavement leave
• time off to vote
• jury duty leave
• volunteer time off
• military leave
• parental leave
• health care insurance (medical, dental, vision)
• retirement planning (401(k))
• paid days off (sick leave, parental leave, flexible vacation/wellness days, and/or PTO)