Staff Data Engineer

The Role

Apptegy is building the data platform that powers decision-making across a high-growth SaaS business. As our Staff Data Engineer, you will design, build, and evolve the systems that move, transform, and serve data across the company — 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 operate as the most senior hands-on engineer on the team, shipping foundational pipelines and platform primitives, setting the engineering standards a small, high-performing team builds against, and raising the bar for reliability, performance, and cost of everything the data platform delivers.

The right person for this role brings deep Snowflake expertise, strong systems judgment, and a track record of shipping production data infrastructure at scale. They are equally comfortable making pragmatic build-versus-buy calls, writing production dbt or Coalesce transformations, tuning Snowflake warehouses, and mentoring senior engineers through design reviews. This person will also help build the platform capabilities that make our data AI-ready — enriched, well-modeled, and reliable enough to support LLM, RAG, and agent-based use cases across the business.

What You'll Do

Platform & Pipeline Engineering

• Design, build, and own high-impact data pipelines and platform primitives across ingestion, storage, transformation, and consumption, with Snowflake as the core platform.
• Build and evolve the medallion architecture across Bronze, Silver, Gold, and Semantic layers, implementing the standards for schema design, object naming, access patterns, and layer boundaries in production.
• Ship the semantic models that represent key business entities, metrics, and KPIs in Snowflake and expose them cleanly to BI tools.
• Lead engineering execution across the modern data stack, including ingestion through Fivetran, transformation through Coalesce and related tooling, and BI delivery through Tableau, ensuring cohesion across the full platform.
• Identify and resolve performance, scalability, and cost issues in Snowflake — query optimization, clustering strategy, warehouse sizing, and storage management — and codify the patterns so the rest of the team can apply them.
• Build the platform capabilities that make data AI-ready, including metadata enrichment, contextual retrieval patterns, and curated data products that support LLM, RAG, and AI agent use cases.

Reliability, Governance & Data Quality

• Implement and enforce data governance in production, including naming conventions, data contracts, PII handling, access controls, lineage, and documentation.
• Build the data observability practice end to end — freshness monitoring, anomaly detection, pipeline reliability, SLA tracking, and incident response.
• Partner with security and other stakeholders to ensure the platform meets compliance, risk, and regulatory requirements.
• Maintain the technical documentation, data flow diagrams, decision records, and data dictionary that keep the platform legible and durable as the team scales.

Technical Leadership

• Act as the senior engineering voice on the data team — driving design reviews, resolving technical ambiguity, and setting the standards other engineers build against.
• Evaluate new tools and platforms and lead build-versus-buy decisions with clear trade-off analysis and strong technical rationale.
• Partner with the VP of Data & Analytics on roadmap execution, prioritization, and platform partnerships across Snowflake, Tableau, Fivetran, and related vendors.
• Mentor Data Engineers and Analytics Engineers through pairing, code review, and hands-on support on the hardest implementation work.
• Communicate technical direction clearly to engineering leaders, business stakeholders, and executive partners.

Cross-Functional Partnership

• Collaborate with stakeholders across the company to understand evolving data requirements and turn them into scalable, well-modeled, maintainable pipelines and data products.
• Partner with the broader engineering organization to define data contracts at system boundaries and build durable product data integration patterns.
• Work closely with BI and analytics consumers, including analysts and Tableau users, to ship data products that enable self-service, consistency, and reduced ad hoc friction.
• Collaborate with Data Scientists and AI/ML Engineers to deliver well-contextualized, AI-ready data as a platform capability, not as bespoke prep work.

What You'll Bring

Required

• 8+ years of experience in data engineering, analytics engineering, or platform engineering roles, with a track record of shipping production data infrastructure at Staff or Principal level.
• Expert-level Snowflake proficiency, including schema design, performance tuning, virtual warehouse management, RBAC, data sharing, and cost governance.
• Deep hands-on experience with modern data transformation practices, including strong design principles and production experience with Coalesce (or equivalent, e.g. dbt, with the ability to lead our Coalesce implementation).
• Strong data modeling fundamentals — dimensional modeling, normalization, entity-relationship design, and semantic layer concepts — applied in production.
• Demonstrated experience building and operating a medallion or layered architecture at scale across multiple business domains.
• Production experience with data governance — access controls, PII classification, lineage, documentation, and data contracts.
• Experience building or operating a data observability practice, including freshness monitoring, anomaly detection, SLA definition, and incident response.
• Expert-level SQL across analytical workloads, with the ability to review, optimize, and improve complex query patterns and mentor others on the same.
• Excellent communication skills, including the ability to write clear technical designs and decision records and present direction to non-technical leadership.
• Working understanding of enterprise AI application patterns, including RAG pipelines, vector search, and agent frameworks, and how the data platform layer supports these use cases with properly contextualized, enriched data.
• Familiarity with how AI and ML workflows consume and depend on data platform capabilities — semantic context, retrieval patterns, and governed access to reliable enterprise data.

What Makes You Stand Out

• Experience building and operating both inbound and outbound connectors along with the observability patterns needed to keep data moving reliably across systems.
• Hands-on experience with Tableau as a BI consumption layer, including how Tableau interacts with data sources and which implementation 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 work.
• Prior experience leading tooling evaluations or vendor selection processes for data platforms, BI, or pipeline tooling.