Staff Data Architect

Apptegy

About The Position

Apptegy is building the data platform that powers decision-making across a high-growth SaaS business. As our Staff Data Architect, you will define and own the enterprise data architecture that enables the business to operate with confidence at scale. This includes how data is ingested, modeled, governed, secured, and consumed across the organization, with Snowflake at the core of the platform. This is a high-impact, high-autonomy individual contributor role reporting directly to the VP of Data & Analytics. You will serve as the technical authority for how data is structured and used across the company, setting the architectural vision for the Business Data Platform and guiding the standards that allow a small, high-performing team to move quickly without compromising quality, governance, or long-term scalability. The right person for this role brings deep Snowflake expertise, strong architectural judgment, and a clear point of view on modern data platform design. They are equally comfortable defining long-range strategy, making consequential build-versus-buy decisions, and working hands-on with engineers to shape implementation details. This person will also help define how the platform serves as the enterprise context layer for AI, ensuring data is enriched, well-modeled, and reliable enough to support LLM, RAG, and agent-based use cases across the business.

Requirements

8+ years of experience in data engineering, analytics engineering, or data architecture roles, with at least 3 years in an architecture-focused capacity.
Expert-level Snowflake proficiency, including schema design, performance tuning, virtual warehouse management, RBAC, data sharing, and cost governance.
Deep experience with modern data transformation practices, including strong design principles and hands-on experience with Coalesce. This role requires someone who can help shape and scale our transformation layer using Coalesce as a core part of the platform.
Strong data modeling fundamentals, including dimensional modeling, normalization, entity-relationship design, and semantic layer concepts.
Demonstrated ability to design and govern a medallion or layered architecture at scale across multiple business domains.
Experience with data governance, including access controls, PII classification, lineage tracking, documentation standards, and data contract expectations.
Experience designing or implementing a data observability practice, including freshness monitoring, anomaly detection, SLA definition, and incident response for data pipelines.
Strong SQL across analytical workloads, with the ability to review, optimize, and improve complex query patterns.
Excellent communication skills, including the ability to create architecture diagrams, write clear decision records, and present technical direction to non-technical leadership.
Understanding of enterprise AI application patterns, including RAG pipelines, vector search, and agent frameworks, and how to design the data platform layer to reliably serve these use cases with properly contextualized, enriched data.
Familiarity with how AI and ML workflows consume and depend on data platform capabilities, including semantic context, retrieval patterns, and governed access to reliable enterprise data.

Nice To Haves

Experience managing both inbound and outbound connectors, along with the observability patterns needed to support reliable movement of data across systems.
Familiarity with Tableau as a BI consumption layer, including how Tableau interacts with data sources and which architectural choices affect performance, usability, and long-term maintainability.
Experience in a SaaS business environment, ideally with exposure to GTM, finance, or product analytics data domains.
Familiarity with Snowflake-native AI capabilities such as Cortex or related platform features that support contextual retrieval and AI-enabled data experiences.
Exposure to unstructured data handling, embedding-based retrieval patterns, or related approaches that intersect with modern enterprise data platform design.
Prior experience leading tooling evaluations, vendor selection processes, or contract discussions related to data platforms, BI, or pipeline tooling.

Responsibilities

Define and evolve the enterprise data architecture across ingestion, storage, transformation, semantic modeling, and consumption layers, with Snowflake as the core platform.
Design and own the medallion architecture across Bronze, Silver, Gold, and Semantic layers, establishing clear standards for schema design, object naming, access patterns, and layer boundaries.
Lead semantic model strategy by defining how key business entities, metrics, and KPIs are represented in Snowflake and surfaced through BI tools.
Drive architectural decisions across the modern data stack, including ingestion through Fivetran, transformation through Coalesce and related conventions, and BI delivery through Tableau, ensuring cohesion across the full platform.
Identify and resolve performance, scalability, and cost-efficiency challenges in Snowflake, including query optimization, clustering strategy, virtual warehouse sizing, and storage management.
Design the platform to serve as a reliable enterprise context layer for AI by establishing patterns for metadata enrichment, semantic data modeling, contextual retrieval, and curated data products that support LLM, RAG, and AI agent use cases.
Establish and enforce data governance standards, including naming conventions, data contracts, PII handling, access controls, lineage expectations, and documentation practices.
Define the organization’s approach to data observability, including freshness monitoring, anomaly detection, pipeline reliability standards, SLA expectations, and incident response patterns.
Partner with security and other stakeholders to ensure the data platform meets compliance, risk, and regulatory requirements.
Build and maintain architectural reference documentation, data flow diagrams, decision records, and the data dictionary that support platform clarity and long-term maintainability.
Act as the senior technical voice on the data team, guiding architectural decisions across active initiatives, resolving ambiguity, and setting long-term direction for the platform.
Evaluate and recommend new tools, platforms, and approaches, leading build-versus-buy decisions with clear trade-off analysis and strong technical rationale.
Partner closely with the VP of Data & Analytics on roadmap planning, prioritization, and strategic vendor relationships, including Snowflake, Tableau, Fivetran, and related platform partners.
Provide hands-on architectural guidance to Data Engineers and Analytics Engineers through design reviews, standards setting, and active support on complex implementation work.
Communicate architectural vision, platform strategy, and technical decisions clearly to engineering leaders, business stakeholders, and executive partners.
Collaborate with stakeholders across the company to understand evolving data requirements and translate them into scalable, well-modeled, maintainable solutions.
Partner with the broader engineering organization to define data contracts at system boundaries and support durable product data integration patterns.
Work closely with BI and analytics consumers, including analysts and Tableau users, to ensure the architecture enables self-service, consistency, and reduced ad hoc friction.
Collaborate with Data Scientists and AI/ML Engineers to ensure the platform surfaces well-contextualized, AI-ready data, serving as the enterprise context layer for LLM, RAG, and AI agent use cases rather than requiring bespoke data preparation work.