Solutions Architect
About the role
You own the shape of an engagement end to end. You profile the workload, design the target architecture, work out how it integrates with what the client already runs, and defend the decisions with their technical leadership.
You work deeply with a small number of clients at a time — not a portfolio of accounts you check in on. Depth is what we offer.
This is a player-coach role. You are hands-on first — working in the schema, designing integrations, and building prototypes yourself. You also lead a delivery team of two to four engineers and are accountable for what they ship.
The patterns you set will become part of how Granule works.
About you
You are an architect who still wants to build, and you are as comfortable with clients as you are with the systems behind them.
You are hands-on. You want to profile the workload, test the design, and build alongside the team rather than review from a distance.
You exercise commercial judgment. You can separate what a client needs from what they are willing to buy — and tell them when the smaller engagement is the right one.
You can disagree without theater. You are comfortable giving technical leadership an answer they may not want to hear, and changing your own view when the evidence says you should.
You own the outcome. If a design you approved does not hold up, you want to understand why and fix it.
What you'll own
Establish what is actually happening: the current architecture, query shapes, cardinality, concurrency, ingest rate, retention, cost, and what the system has to read to answer a question.
Design the engine, topology, ingestion path, semantic layer, governance model, and how it integrates with the client's existing data, BI, orchestration, identity, applications, and agents.
Lead two to four engineers, turn the architecture into sequenced work, review the decisions that matter, unblock the team, and stay accountable for quality and delivery.
Help scope discovery, proposals, SOWs, estimates, and the cost model behind them — including what we are willing to fixed-price and what we are not.
Turn what works into reference architectures, integration patterns, estimation standards, accelerators, and delivery playbooks that the rest of the firm can reuse.
The table stakes
Skills and experience get you in the room. What you do with them is what gets you the job.
- Senior data architecture experience. 8+ years in data, analytics engineering, or architecture, including meaningful client-facing work and responsibility for other people's output.
- Deep analytical database experience. Real production depth in at least one columnar or MPP system such as ClickHouse, Druid, Pinot, StarRocks, Snowflake, BigQuery, Redshift, or Vertica. ClickHouse experience is preferred; the ability to get deep on an unfamiliar engine quickly also counts.
- Enterprise and real-time architecture. You have designed systems spanning source platforms, warehouses, BI, orchestration, identity, and applications, with production experience in Kafka or a comparable streaming platform.
- Semantic layer and governance. You have put shared business definitions and governed access into production — including permissions, data isolation, and the controls needed to expose data safely across teams and applications.
- Hands-on technical fluency. Strong SQL and Python, cloud experience in AWS, GCP, or Azure, and practical familiarity with infrastructure tooling such as Terraform and Kubernetes.
- Commercial and AI judgment. You have built a defensible cost model for a data platform and use AI coding tools as part of your real workflow, with good judgment about when to trust, verify, or discard their output.
- Education. Bachelor's degree in computer science, engineering, or a related field — or equivalent practical experience.
What sets you apart
None of these are required, but they tell us where you may bring unusual depth.
- Deep ClickHouse experience. You have run ClickHouse or ClickHouse Cloud in production, tuned it under real load, or contributed meaningful work back to the project.
- Semantic and governance depth. You have operated a semantic layer in production and dealt with the hard parts: conflicting metric definitions, row- and column-level access, multi-tenant isolation, quotas, spend controls, or policy enforcement.
- Agent-facing data architecture. You have exposed governed enterprise data to AI systems through retrieval, vector search, RAG, MCP, or similar patterns — and understand where semantics, permissions, and query economics become harder when agents are the consumer.
- Observability at scale. You have designed or migrated high-volume logging, metrics, or tracing platforms using tools such as OpenTelemetry, Vector, ClickStack, Datadog, Splunk, Elastic, or New Relic.
- Large-system migration experience. You can describe a consequential migration — including the part that went badly, what you learned, and what you would do differently.
- Domain depth where the data gets difficult. Adtech, fintech, gaming, IoT, telecom, security telemetry, or another environment where volume, cardinality, latency, or retention materially changed the architecture.
- Visible technical craft. Conference talks, technical writing, open-source work, benchmarks you published and defended, or a GitHub history that shows how you think.
- Evidence of continued learning. Relevant ClickHouse, cloud, Kafka, dbt, Snowflake, or Databricks certifications — or other evidence that you continue going deep rather than relying on experience alone.
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