avanto is an innovative and forward-thinking company transforming the furniture industry through technology and a people-centric approach.
we stand out for our cutting-edge solutions, exceptional service, and strong commitment to excellence, integrity, and innovation.
about the role
the technical business analyst is the bridge between ambiguous business intent and deterministic, buildable specifications for avantodev's data and agentic projects.
our platform turns standard operating procedures into executable agent logic ("code = sop"), and that translation starts with you.
you deconstruct messy business processes — quote-to-install workflows, document-processing pipelines, master-data validation rules — into precise requirements that data engineers and ai engineers can implement and test.
this is not a "write user stories in jira" role.
you will own spec-driven development (sdd) artifacts end-to-end: ears-formatted requirements, gherkin acceptance scenarios that define "definition of done" for probabilistic agents, data dictionaries and schema specifications, source to-target mappings, and the business-rule catalogs that become guardrails.
you sit at the center of high-entropy data work — defining what "correct" means for a po, an ack, an invoice, or a tenant-specific discount rule — and you make sure the rest of the team builds the right thing.
what you'll own
requirements & spec-driven development
deconstruct complex business processes and user stories into ears-formatted requirements (easy approach to requirements syntax) that are unambiguous, testable, and traceable.
author gherkin scenarios (given/when/then) that define the "definition of done" — including for non-deterministic agent behavior, where you specify the logic path, not just the output.
maintain the master contextual package (mcp) traceability: every requirement maps to a business need and to the agent/pipeline logic that implements it.
data specification & analysis
build and maintain data dictionaries, schema specifications, and source-to-target mappings for the bronze → silver → gold pipeline and the postgresql schema registry.
define field-level validation rules, tolerances, and alias dictionaries (e.g., "qty shipped" → quantity) that drive the schema matching and validation mcp servers.
profile source data, quantify data-quality issues, and write the acceptance criteria for data-quality gates and cross-field validation (e.g., line items sum to total).
business-rule & guardrail catalogs
translate business policy into structured, machine-consumable business-rule definitions (discount limits, tax rules, workflow steps) and the guardrail specs that enforce them ("block discount > 20%").
maintain the knowledge-pack catalogs (business rules, guardrails, knowledge base) as living, versioned specifications.
process & workflow modeling
map current-state and future-state quote-to-install and document-processing workflows, identifying automation opportunities, hitl (human-in-the-loop) decision points, and exception paths.
define confidence-based routing rules with stakeholders (≥90% auto-approve, *% review).
stakeholder translation & acceptance
run requirements workshops and translate technical complexity (why an automation is risky, costly, or non-deterministic) into language non-technical stakeholders can act on.
own uat planning and acceptance: build test cases from the gherkin specs, coordinate sign-off, and verify delivered work matches the spec.
produce operational reporting specs — defining the metrics (throughput, accuracy, cost-per-document, net savings) and dashboards the business needs.
qualifications
5+ years as a business analyst / technical ba, with 3+ years on data-intensive projects (data platforms, integrations, analytics, or document/idp processing).
strong sql — able to independently profile data, validate hypotheses, and write moderately complex queries against postgresql or similar.
demonstrated requirements engineering skill — user-story decomposition, acceptance criteria, and at least one formal technique (ears, bdd/gherkin, use cases, or equivalent).
data modeling literacy — able to read and contribute to data dictionaries, schema specs, er diagrams, and source-to-target mappings.
understands normalized vs. denormalized models conceptually.
process modeling — bpmn or equivalent; mapping current/future-state workflows and identifying automation and exception paths.
familiarity with the modern data & ai stack — understands what rag, vector databases, apis/microservices, and llm-based automation are and where they fit (you don't have to build them, but you must spec for them).
tooling — jira, confluence/notion, and diagramming tools; comfortable maintaining traceability matrices and living documentation.
english proficiency: b2+ required (c1 preferred).
you'll facilitate workshops, write specs, and translate between business and technical audiences daily.
preferred skills
experience specifying agentic / llm-driven systems — defining "definition of done" for non-deterministic outputs and writing guardrail/policy requirements.
hands-on with document/idp projects — pos, acks, invoices, schema/alias matching, confidence scoring, and hitl review workflows.
familiarity with spec-driven development (sdd) and the model context protocol (mcp) concept.
experience writing yaml/json business-rule definitions or configuration-as-spec.
light scripting (python) for data profiling and analysis.
background in commercial furniture, logistics, distribution, or manufacturing operations.
domain exposure to quote-to-install / order management processes.
success criteria
less rework — engineers build the right thing the first time because requirements are unambiguous, testable, and traceable.
specs are executable — gherkin acceptance criteria map cleanly to automated tests, including for agent behavior.
data is well-defined — data dictionaries, mappings, and validation rules are complete and current; "what does correct mean?" Always has a documented answer.
business rules are governable — policies are captured as structured, versioned rule/ guardrail specs rather than tribal knowledge.
stakeholders are aligned — non-technical stakeholders understand scope, risk, and trade-offs; sign-off is smooth and uat defects are caught before production.
delivery velocity improves — well-formed specs reduce mid-sprint ambiguity and the velocity of requirements moving from backlog to done increases.
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