Customer Support Analyst

• How petabyte-scale data infrastructure works - Allium manages 5+ petabytes across every combination of cloud provider x data warehouse (Snowflake, BigQuery, Databricks...) x region. You'll pick up how these systems are designed, where they break, and how to reason about freshness, cost, and correctness tradeoffs between them.
• How to build AI-native support - Tickets that feed runbooks; runbooks that support agentic skills; an agentic loop that builds on itself and compounds - You will learn how to build an AI-native support system that scales.
• How to manage expectations with institutional customers (banks, asset managers, exchanges, foundations) - What incident management best practices look like in production, how to communicate during a data incident, how to set an SLA you can actually keep, and how to thread the needle when two customers' expectations conflict.
• How 100+ blockchains work under the hood - Resolving a ticket often means tracing a number back to raw on-chain data - why a stablecoin supply figure differs between two sources, why a balance snapshot lags, what a reorg does to a table. You'll build genuine depth in EVM, Solana, and the long tail of chains.
• How enterprise data delivery works end to end - warehouse-native shares, APIs, Kafka streams, push vs pull. You'll learn to pick the right mechanism for a customer's problem, because half of support is knowing where in the product the answer already lives.
• How to run a data quality investigation - verification checks, cross-source reconciliation, and separating "the data is wrong" from "the query is wrong" from "the chain did something unusual."
• A path into becoming an , if that's where you want to go. Every Wizard at Allium started on the frontlines; we believe staying close to customers and their use cases is how you learn the data best. This role builds exactly those skills: tracing numbers back to raw on-chain data, debugging SQL at scale, learning protocol mechanics.

What You’ll Do

• Troubleshoot and resolve complex customer issues involving SQL queries, data pipelines, API integrations and other Allium Integrations (e.g. kafka)
• Mapping customer issues to where the solution exists in Allium. Be it a table in the dataset, another delivery mechanism, relevant documentation, another integration pattern (e.g. push vs pull)
• Help customers diagnose problems in their data sources (Snowflake, BigQuery, Postgres, etc.) and optimize performance.
• Collaborate with engineering to investigate bugs, identify patterns, and ship fixes.
• Develop internal tools and scripts to automate troubleshooting and improve customer response times.
• Write clear documentation and knowledge base articles for customers and internal teams.
• Partner with the GTM and product teams to translate customer pain points into actionable insights.
• Participate in daily syncs to discuss ongoing issues, progress, and improvements.
• Synthesize all of the above into an AI-native system that builds leverage.
• Triage and prioritize incoming issues against customer SLAs — distinguishing a P1 data incident for an institutional customer from a routine query question, and sequencing your work so the highest-impact problems get resolved first.

What You’ll Bring

• Fluency in AI tools, common failure modes and modern practices.
• Fluency in SQL - you can confidently write, optimize, and debug complex queries.
• Experience providing technical support for a SaaS or data product.
• A working knowledge of blockchain data primitives across ecosystems like Solana and EVM chains.
• Familiarity with APIs, data connectors, and ETL tools.
• Comfort with scripting or automation in Python, Bash, or similar languages.
• Strong understanding of Linux environments and command-line tools.
• Excellent communication and documentation skills — you can explain complex concepts simply.
• A proactive, empathetic approach to customer relationships.
• The ability to work independently and take full ownership of issues from report to resolution.

Bonus Points

• Experience with data warehouses (Snowflake, BigQuery, Redshift) or BI tools.
• Exposure to infrastructure-as-code tools (Terraform, Ansible, etc.).
• Experience in debugging API or integration issues across cloud environments.
• Familiarity with data modeling or transformation frameworks (dbt, Airbyte, etc.).
• Experience with message queues and event buses (Google Pubsub, Kafka etc)