Snowflakes (Remote)

Trace3•Atlanta, GA

8d•$170,000 - $200,000•Remote

About The Position

Trace3 is seeking a Data Architect to join the Data and Analytics Services team. This role combines hands-on architecture, technical leadership, consulting, and delivery responsibility. The Data Architect will help clients design, modernize, govern, and operationalize data platforms that support analytics, artificial intelligence, reporting, and business decision-making. The ideal candidate can move comfortably between executive conversations, architecture workshops, detailed technical design, and delivery oversight. You will establish practical architecture standards, guide engineering teams, evaluate platform trade-offs, and help ensure that solutions are secure, scalable, maintainable, and aligned to business outcomes. This is a client-facing role with responsibility for both solution quality and team enablement.

Requirements

8+ years of experience in data architecture, data engineering, analytics engineering, or a related technical consulting role.
Hands-on production experience with at least two of these three data platforms: Snowflake, Databricks, and Microsoft Fabric.
Demonstrated experience designing and delivering enterprise or large-scale cloud data platforms.
Strong knowledge of data modeling, data integration, lakehouse and warehouse patterns, analytics architecture, and data governance.
Proficiency with SQL and Python, including the ability to review and guide production-quality code.
Experience with data pipeline orchestration, CI/CD, source control, automated testing, and environment promotion.
Experience leading architecture workshops, technical design sessions, or complex delivery initiatives.
Ability to explain technical decisions, alternatives, risks, and business impact to varied audiences.
Demonstrated ability to mentor technical teams and establish repeatable engineering standards.
Willingness and ability to travel periodically for client engagements, team meetings, or industry events.

Nice To Haves

Experience with all three target platforms: Snowflake, Databricks, and Microsoft Fabric.
Experience with Azure, AWS, or Google Cloud data services.
Familiarity with Power BI, dbt, Kafka, Event Hubs, Airflow, Azure Data Factory, Terraform, Kubernetes, or comparable technologies.
Experience implementing data products, semantic layers, master data, metadata, data catalogs, or data contracts.
Experience with AI-ready data foundations, machine learning platforms, generative AI, or agent-consumable data architectures.
Experience with FinOps, workload optimization, platform operating models, or managed data services.
Relevant certifications in Snowflake, Databricks, Microsoft Fabric, Azure, AWS, Google Cloud, data management, or enterprise architecture.
Experience in a professional services, systems integration, or client-facing consulting environment.

Responsibilities

Lead the design of modern data architectures across cloud, hybrid, and on-premises environments.
Translate business, information, analytics, and AI requirements into durable technical architectures and implementation roadmaps.
Define patterns for data ingestion, transformation, orchestration, storage, modeling, serving, governance, security, and observability.
Design lakehouse, data warehouse, data mesh, data product, and hybrid architectures as appropriate to client needs.
Establish clear separation of responsibilities across raw, refined, curated, and consumption layers.
Define standards for dimensional modeling, semantic models, master and reference data, slowly changing dimensions, data quality, lineage, and metadata.
Evaluate architecture options and communicate trade-offs using clear decision criteria, risks, and consequences.
Produce architecture diagrams, standards, reference architectures, technical designs, and implementation plans.
Bring hands-on experience with at least two of the following platforms: Snowflake, Databricks, and Microsoft Fabric.
Apply platform-native capabilities effectively, including Snowflake data engineering and governance, Databricks Lakehouse and Unity Catalog, or Microsoft Fabric OneLake, Data Factory, and Power BI integration.
Assess platform fit and recommend appropriate technologies based on business requirements, operating model, skills, cost, performance, security, and time to value.
Establish scalable patterns for batch, streaming, event-driven, and incremental data processing.
Design for performance, resiliency, cost transparency, and operational supportability.
Guide teams in productionizing notebooks, pipelines, SQL, Python, reusable frameworks, and infrastructure-as-code.
Define environment promotion and CI/CD practices across development, test, and production environments.
Facilitate discovery sessions, architecture workshops, design reviews, and stakeholder working sessions.
Build trusted relationships with client executives, technology leaders, data leaders, engineers, analysts, and business stakeholders.
Lead current-state assessments and maturity evaluations, then develop prioritized recommendations and roadmaps.
Communicate complex technical concepts clearly to both technical and non-technical audiences.
Contribute to proposals, statements of work, estimates, presentations, demonstrations, and solution shaping.
Identify delivery risks early and partner with project and client leadership to resolve them.
Ensure architecture decisions remain connected to measurable client outcomes, adoption plans, and delivery realities.
Provide technical direction to data engineers, analytics engineers, architects, analysts, and client teams.
Lead architecture reviews and design governance throughout the delivery lifecycle.
Mentor engineers and architects through coaching, pairing, feedback, and knowledge sharing.
Create reusable accelerators, templates, standards, playbooks, and service offerings for the Data and Analytics practice.
Promote engineering practices that reduce technical debt, improve maintainability, and enable teams to operate solutions independently.
Help shape Trace3’s data architecture capabilities, methodologies, and go-to-market offerings.
Incorporate data security, privacy, access control, compliance, retention, and responsible data use into architecture decisions.
Design governance models that balance centralized standards with practical delivery autonomy.
Establish approaches for data quality monitoring, lineage, cataloging, documentation, and stewardship.
Define operational requirements for monitoring, alerting, incident response, disaster recovery, and service-level objectives.
Evaluate and improve platform cost management, capacity planning, workload isolation, and resource utilization.