Data Architect — Snowflake & AWS (Presales + Delivery)

Level: Senior / Principal · Client-facing · Anchor architect for the Snowflake–AWS stack

About the Role

We build data platforms, accelerators, and AI agents for enterprise clients — and every one of them stands on an architecture someone had to get right the first time. As Data Architect for our Snowflake + AWS stack, you will own that architecture across the engagement lifecycle: shaping the solution in presales, defending it in client workshops, and staying accountable for it through delivery.

This is a dual-hat role by design, roughly balanced between presales and delivery. In pursuit mode, you are the technical voice in RFP responses, discovery workshops, and proofs of concept — the person who turns a client's problem statement into a credible, estimable, winnable solution. In delivery mode, you set the architecture, review what gets built against it, and make the judgment calls that keep platforms performant, governed, and within their Snowflake credit budget. Architects who design decks they never have to live with will not enjoy this role; architects who want their proposals held to account will.

What You Will Do — Presales & Solutioning

Lead technical discovery with prospective clients: current-state assessment, workload analysis, and translating business requirements into target-state Snowflake + AWS architectures.
Own the solution architecture in RFP/RFI responses and proposals: architecture diagrams, phased roadmaps, migration approaches, effort and team-shape estimation, and the assumptions register that protects delivery later.
Size and defend the commercial-technical envelope: Snowflake credit and warehouse sizing, AWS consumption estimates, and TCO comparisons against incumbent or competing platforms — with rationale that survives procurement scrutiny.
Design and lead proofs of concept and pilots that are scoped to prove the contested claim, not to demo the easy path.
Present to audiences from data engineers to CIOs: whiteboard the architecture, take hostile questions, and communicate trade-offs in business terms.
Work the partner ecosystem: align with Snowflake and AWS field teams (SEs, partner managers) on joint pursuits, funding programs, and competency requirements.
Feed the practice: convert pursuit patterns into reusable solution blueprints, estimation models, and accelerator requirements.

What You Will Do — Architecture & Delivery

Define end-to-end data architectures on Snowflake and AWS: ingestion (Snowpipe, streams and tasks, Kafka/Kinesis, DMS, Glue), storage and modeling layers, transformation (dbt, Snowpark), and consumption (BI, data sharing, APIs, AI/agent workloads).
Set the data modeling standards for engagements: dimensional and lakehouse patterns, medallion layering, and when each is overkill.
Design the governance and security model: RBAC hierarchies, masking and row-access policies, tagging, data classification, and audit posture — aligned to client regulatory contexts.
Own performance and cost discipline: warehouse right-sizing, query optimization, clustering and materialization decisions, resource monitors, and credit-burn reviews as a standing practice, not a rescue mission.
Architect migrations to Snowflake from legacy warehouses (Teradata, Oracle, SQL Server, Hadoop) and between clouds, including cutover and reconciliation strategy.
Design for interoperability: external tables and Iceberg, cross-cloud data sharing, and integration with the AWS-native estate (S3, Glue Catalog, Lambda, Step Functions, EventBridge, IAM).
Prepare data foundations for AI consumption: semantic layers, documentation and metadata that agents can reason over, and patterns for exposing governed Snowflake data to LLM and agent workloads.
Review delivery against the architecture: design authority for engineering teams, unblocking the hardest technical decisions, and keeping as-built honest to as-sold.

Must-Have Qualifications

12+ years in data engineering and architecture, with 4+ years architecting production Snowflake platforms at enterprise scale — including at least one greenfield build and one migration.
Deep, hands-on Snowflake expertise: performance tuning, cost governance, security and RBAC design, data sharing, Snowpark, and the trade-offs behind each — depth we will test technically, not by certification.
Strong AWS proficiency across the data estate: S3, Glue, Lambda, Kinesis/MSK, DMS, Step Functions, IAM, and network/security patterns (PrivateLink, VPC design) for enterprise Snowflake deployments.
Demonstrated presales experience: solutions you architected in winning bids, PoCs you led, and estimates you produced that delivery teams subsequently lived with. We will ask for specifics — deal shapes, your role, and what you got wrong.
Expert SQL and working Python; fluency with dbt or an equivalent transformation framework.
Mastery of data modeling: dimensional, data vault or lakehouse patterns, and the judgment to choose between them per workload.
Executive-grade communication: you can hold the room in a CIO briefing and a deep-dive with the client's DBAs on the same day.

Good to Have

SnowPro Advanced Architect and/or AWS Solutions Architect Professional certification.
Experience with Snowflake's AI capabilities (Cortex, document AI) or exposing warehouse data to LLM/agent workloads (text-to-SQL, semantic models).
Apache Iceberg and open table format strategy experience.
Data catalog, lineage, and observability tooling (e.g., Atlan, Collibra, Alation, Monte Carlo).
Streaming architecture depth (Kafka/Kinesis at scale, CDC patterns).
Consulting background with multi-client delivery; supply chain, manufacturing, or ERP (SAP/Oracle) data domain exposure.
Contributions to reusable IP: solution blueprints, estimation frameworks, migration toolkits, or published technical content.

Data Architect Snowflake & AWS (Presales + Delivery)