Sr. Consultant, Data Architect

We are looking for a Sr. Consultant, Data Architect to join our growing team of experts. This position owns the technical architecture of client engagements on modern cloud data platforms, with Snowflake and Databricks and cloud-native services on AWS, Azure, and GCP alongside it. The work spans target-state platform design, data modeling, ingestion and transformation patterns, security and governance design, and the engineering standards that keep delivery consistent from first design through production.

The ideal candidate is as comfortable whiteboarding an architecture as they are rolling up their sleeves in delivery. They have designed and delivered enterprise data platforms end to end, lead discovery with clients to turn business goals into technical requirements, and make and document the trade-offs behind every design decision. They incorporate AI tooling into how they work and deliver, can advise clients on where AI fits in their data strategy, and architect platforms that support AI and analytics workloads while keeping foundational data quality, structure, and governance intact.

This role carries technical leadership on an engagement. The Solution Architect is accountable for the technical outcome: the architecture is sound, the team builds to it, and what ships to the client meets the design. They serve as the primary technical point of contact for client stakeholders, set technical direction for the delivery team, run design and code reviews, and mentor engineers at earlier stages of their careers. This person thrives in a consulting environment where no two engagements are the same.

This role can be performed from anywhere in the US.

Responsibilities:

•    Discovery and Solution Design: Lead discovery sessions with client stakeholders to clarify business goals, technical requirements, and constraints, and translate them into a production-ready data platform architecture with a phased delivery plan.

•    Platform Architecture: Design platform topology, environment strategy, security and governance model (role-based access, masking, row-level policies, tagging), and cost and performance architecture for the engagement's platform.

•    Data Modeling: Design layered data models (medallion, dimensional, Data Vault) and semantic layers that serve BI, application, and AI consumption.

•    Integration and Pipeline Patterns: Select integration patterns (batch, CDC, event-driven, file-based, API, data sharing) against client constraints, and document the trade-offs in architecture decision records.

•    Transformation and Engineering Standards: Lead model design, materialization strategy, testing, and documentation in dbt or an equivalent framework; establish CI/CD, coding standards, and peer review for the team.

•    Cloud Integration: Architect integrations between the data platform and cloud services, including object storage, IAM, networking and private connectivity, secrets management, and managed processing services.

•    Platform-Native and AI Capabilities: Evaluate and adopt platform-native capabilities where they fit the client's roadmap (for example Snowflake Cortex, Snowpark, Iceberg and open table formats, Databricks Unity Catalog), and design the data foundations that AI and ML workloads depend on.

•    Delivery Ownership: Own technical delivery to production: decompose the architecture into workstreams, review what the team builds against it, monitor pipeline SLAs and platform cost, and lead root-cause analysis for data incidents.

•    Client Engagement: Serve as the primary technical point of contact for client stakeholders and executives; lead design sessions and technical status reporting; surface risks, blockers, and scope changes early with proposed resolutions.

•    Team Leadership and Mentorship: Set technical direction for data engineers and analytics engineers on the engagement, run design and code reviews, and develop teammates through pairing and structured feedback.

•    Practice Contribution: Contribute reusable accelerators, patterns, and enablement content back to the practice, and support solutioning and estimation for new work.

•    AI-Assisted Delivery: Use AI-assisted engineering tooling in design and delivery work and set standards for its use within the team.

This job can be performed from anywhere in the US.

•    Bachelor's degree in Computer Science, Engineering, or an equivalent field.

•    8+ years in data engineering, data warehousing, or solution architecture, including 2+ years as the lead architect on enterprise data platform engagements.

•    Client-facing consulting or professional services delivery experience, including leading discovery and design sessions with technical and business stakeholders.

•    3+ years hands-on on at least one modern cloud data platform: Snowflake (warehouse topology, clustering and performance, RBAC and security model, Streams, Tasks, Dynamic Tables, data sharing) or Databricks (Delta Lake, Unity Catalog, Spark, Workflows).

•    Enterprise data modeling across medallion, dimensional, and Data Vault or 3NF approaches, and target-state platform design across ingestion, transformation, semantic, and consumption layers.

•    Proven track record designing batch and streaming ingestion frameworks and choosing among integration patterns (CDC, event-driven, file-based, API) based on client constraints.

•    Strong proficiency in SQL and Python; hands-on with ELT and orchestration tooling such as dbt, Matillion, Informatica, Airflow, or platform-native pipelines.

•    CI/CD for data platforms (GitLab, GitHub Actions, or Azure DevOps): pipelines, merge request workflows, environment promotion, and automated testing.

•    Cloud platform experience on AWS, Azure, or GCP, including storage, IAM, networking and private connectivity, secrets management, and cost considerations for data platforms.

•    Experience designing platforms that support AI and ML workloads, including feature and vector data and LLM integration within data pipelines.

•    Demonstrated technical leadership: setting technical direction, conducting design and code reviews, and mentoring engineers at earlier career stages.

•    Strong client-facing communication skills, including presenting and defending architecture decisions to technical and executive stakeholders.

•    Practical use of AI-assisted development tooling to improve design and delivery throughput.

•    Comfortable working in Agile delivery environments.

•    Platform Certifications: SnowPro Advanced: Architect or Data Engineer; Databricks Certified Data Engineer Professional or equivalent.

•    Multi-Platform Architecture: Experience on both Snowflake and Databricks, including coexistence or migration architectures, and with Apache Iceberg or other open table formats.

•    Cloud Architecture Certification: AWS Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Data Engineer.

•    DevOps for Data: Infrastructure as code (Terraform), environment strategy, and observability and monitoring for data platforms.

•    Platform Migration: Experience migrating legacy warehouses (Teradata, Oracle, SQL Server, Hadoop) to cloud platforms, including assessment, wave planning, and validation.

•    Data Governance and Security Architecture: Governance frameworks, data quality controls, lineage, and cataloging (Alation, Atlan, Collibra, or equivalent).

•    Streaming and Event-Driven Patterns: Kafka, Kinesis, EventBridge, or Event Hubs for operational and analytic data movement.

•    Applied AI in Production: Exposure to MLOps practices, model serving, RAG and agentic data patterns, and Snowflake Cortex or Databricks Mosaic AI.

•    Industry Context: Regulated industry experience (financial services, insurance, healthcare and life sciences, energy, public sector).

•    Solutioning and Pre-Sales Support: Contributing architecture, estimates, and technical narrative to proposals and statements of work.