Senior Solutions Architect (Remote - Brazil)

ℹ️ This role is exclusively for candidates based in Brazil. 🇧🇷

You’ll own the technical relationship with our accounts: onboarding, implementation, adoption, escalations, and everything that happens when a pipeline that used to work stops working.

You are the person the customer's data team calls, and the person who tells them the truth when the answer is inconvenient. Most of the job is conversation: understanding what they are actually trying to do, explaining what the platform will and will not do for them, keeping their expectations aligned with reality, and raising problems while they are still small.

The technical part matters, but not in the way people usually assume. We want a generalist who has built things before and therefore understands how the pieces fit together: what a Kubernetes cluster is for, why a VPN tunnel might be the bottleneck, what a stack trace is telling you, what a slow query looks like. You will not be asked to be the deepest engineer in the room. You will be asked to be the person who can talk to the deepest engineer in the room, understand them, and translate what they said to a VP who has thirty seconds.

• Own the technical relationship for a portfolio of accounts. You are the continuity: the person who knows their environment, their history, and their constraints.
• Lead onboarding and implementation. Understand what the customer is trying to build, design the integration with them, and guide it to production.
• Run the hard conversations. A missed date, a degraded pipeline, a limitation they did not expect, a customer who is upset and technically wrong. You stay calm, keep credibility, and land on a next step everyone agrees to.
• Flag risk early and in writing. Adoption stalling, a champion who left, a project quietly losing sponsorship, an architecture heading somewhere that will not scale. Flagging a risk three weeks early is worth more here than fixing it heroically after it lands.
• Translate between audiences. The same situation explained to a data engineer, to a security reviewer, and to a VP, without losing any of them.
• Troubleshoot alongside the customer: failed runs, schema drift, connector and authentication failures, connectivity problems, performance issues. You do the first-line diagnosis and bring engineering a clear reproduction when it needs to escalate.
• Partner with CSM and Support on renewal risk, and hand expansion signals to Sales. 
• Mentor other SAs on how to handle customers, not just how to solve problems.

• You can effortlessly communicate in English, both spoken and written. You're able to walk into difficult meetings and create rapport with your customers.
• You can explain a technical concept to a non-technical stakeholder without either dumbing it down or losing them.
• You handle conflict without escalating it. You can tell a customer no, or "that was our mistake" and keep the relationship intact.
• You build trust over months, not meetings. Customers should ask for you by name.
• You write clearly and unprompted: summaries, next steps, and owners after every meaningful call.
• You raise problems early, including your own. We would much rather hear "this is slipping and here is why" than a confident status update that turns out to be wrong.
• You push back on bad requirements instead of silently building them.

You should be a generalist who has built software or data systems at some point. Not necessarily an expert in any one of these, but genuinely conversant in most of them, in the way someone is conversant after having actually worked with them:

• Python. You can read someone else's script, follow what it does, make a modification, and understand stack traces.
• SQL. Comfortable writing joins, aggregations, and filters. You can look at a slow query and have a sensible first guess about why.
• Data pipelines and integration. APIs and their common failure modes (rate limits, pagination, retries, auth expiry), webhooks, CDC, file-based ingestion, batch versus streaming trade-offs.
• Cloud data platforms. Working familiarity with at least one of Snowflake, Databricks, BigQuery, Redshift, or a major cloud data warehouse.
• Authentication. You understand what OAuth, SSO, service accounts, and API keys are and roughly how they work. You can follow a conversation about a failing token exchange.
• Networking. DNS, firewalls, private connectivity, VPNs and SSH tunnels. You do not need to configure them, but you need to understand the concepts well enough to hold a productive call with a customer's network team and know when the network is the problem rather than the platform.
• Containers and Kubernetes. Conceptual understanding only: what a container is, what Kubernetes does, why workloads run in pods, what it means when something runs out of memory. You will not be administering clusters.
• Linux. Comfortable enough in a terminal to read a log file, check a process, or run a curl command.

• 5+ years in technical customer-facing work: solutions architecture, implementation, technical account management, professional services, consulting, support engineering, or a data engineering role that involved heavy stakeholder contact.
• At least some of that spent hands-on building, not only advising.
• Experience owning accounts or projects through delivery, not just handing off.
• Prior experience working for US-based companies or with US-based customers.

• Prior experience at a data integration, ETL, or data platform vendor.
• Background in data engineering or software engineering earlier in your career.
• Experience in a regulated industry (healthcare, financial services, insurance).
• Entrepreneurial experience or similar (having worked at a fast-paced startup environment).

• Not a support queue. You own outcomes on named accounts.
• Not a deep engineering role.
• Not a pure relationship role either. You need to be technical enough to earn the customer's respect in the first meeting.

1. Recruiter screen (30 min)
2. Hiring manager conversation (45 min)
3. Technical breadth session (60 min): troubleshooting reasoning, plus basic Python and SQL
4. Customer scenario (60 min): you handle a difficult account situation with a panel playing customer roles