Data Analyst

Sentra is the AI Data Readiness platform for enterprises deploying AI at scale. Sentra continuously discovers, classifies, and governs sensitive data across cloud, SaaS, data warehouses, and AI environments - giving security teams complete visibility into what AI systems can access, what they can expose, and where compliance violations exist. Unlike alternatives that copy data to vendor clouds or require customer-managed outpost infrastructure, Sentra provisions ephemeral scanners directly inside the customer's environment, delivering petabyte-scale classification at a fraction of the cost. Trusted by Lyft, SoFi, Munich RE, PennyMac, Glovo and others.

Sentra is hiring its first US Classification Data Analyst to be the go-to expert on data classification for our US customers. You will join the Solutions Architecture team, reporting to the Director of Solutions Architecture, and work closely with our R&D Data Analysts team in Israel.

Classification is what customers judge us on during proofs of value (PoVs) and renewals: do our results match their data? You will make sure the answer is yes. You will own classification requests for US PoVs, validate results before customer calls, tune classifiers using proven procedures, and bring the hard problems to R&D with everything they need to solve them.

This is a high-impact role at a fast-growing startup. You will help build the US classification function from the ground up, and your scope will grow as you take on more of the work.

Responsibilities

• Be the first point of contact for classification requests from US customers and PoVs, working with Sales Engineers, Solutions Architects, TAMs, Support and Customer Success.
• Own classification on PoV engagements: validate results before customer calls and join those calls as the classification expert in the room.
• Tune classification using approved procedures: add and remove keywords, build customer-specific regex and dictionary classes, run re-analysis, review customer-built classes and manage allowlists.
• Find false positives and false negatives before customers do, and explain clearly why something was classified the way it was and what will change.
• Escalate complex issues to the R&D Data Analysts team with a complete, well-written handoff they can act on without a follow-up call.
• Track every request and its outcome, and turn that data into insight on volume, trends, and where we can improve.
• Proactively look across PoVs to spot what is working, what is not, and what needs to change in our classification, our process, or our runbooks.
• Keep our runbooks current: report gaps, propose fixes, and update instructions as the product evolves.

Requirements

• 3+ years in a customer-facing technical role (support engineering, solutions engineering, TAM, data analyst, or similar) at a SaaS or security company.
• Startup experience. You are comfortable with ambiguity, move fast, and take ownership without waiting to be asked.
• A proactive mindset. You look for problems and improvements before they become customer issues.
• Working SQL. You can write and adapt queries in Athena or a similar engine.
• Regular expressions. You can read, write, and test patterns and explain their false positive behavior.
• Git and code literacy. You understand how Git and pull requests work and can read and follow code, such as a Python script. You do not need to be a developer, and using AI tools like Claude to help write code is welcome, as long as you understand what the code does.
• Clear, structured communication. You can explain technical results to a customer and write a handoff that a teammate can act on without a call.
• Location reside on the East Coast USA 

Nice to have

• Familiarity with data classification, DLP or DSPM concepts (PII, PCI, PHI, data classes, false positive rates).
• Exposure to cloud data stores such as S3, Snowflake, Databricks or Microsoft 365, and how sensitive data shows up in them.
• Experience with dashboards (Metabase or similar) and ticketing tools (Shortcut, Pylon or similar).