Developer Analyst
A developer analyst reads code, writes SQL, and debugs production instead of waiting on someone who can. This pillar covers that craft in practice: querying the database to find the truth, reading an API contract without a developer, tracing one transaction through production logs, and using Git, Python, and regex to automate the work you repeat. Written for analysts who want to contribute well beyond requirements, with skills you can apply on your next ticket.
The engineering half of requirements work: YAML with a schema validator, MCP and scoped API tokens for Jira, Confluence, Xray, and Datadog, and coverage gates in CI.
The parts that put a model against real systems: reading a repository you did not write, and writing SQL you can actually defend.
Generate a Mermaid sequence diagram from a Bruno or Postman collection run. The trace script, the newman JSON export, the prompt, and the CI wiring.
Turn manual Kafka checks into an automated Postman collection: produce and consume over REST Proxy, poll with backoff, assert schema and ordering, and run it in CI.
Wire an assistant to Jira, Confluence, Xray, and Datadog with scoped API tokens and MCP: read safely, publish idempotently, and keep every write reviewable in git.
Write business and functional requirements as structured YAML with stable ids, validate them with a schema, and render the BRD and FRD humans read from the same source.
Turn correlated Splunk or Datadog logs into an accurate Mermaid sequence diagram with AI. The queries, the export shape, the prompt, and the verification step.
Write a test strategy that makes decisions, turn conditions into an executable suite, run it in CI with Xray and Datadog, and generate the coverage proof automatically.
How the whole AI analyst stack fits into a real week, the 90-day build order, what to measure, how to lead adoption on your team, and what to put on your CV.
Turn an OpenAPI contract into a Bruno or Postman collection with real assertions, generate the chaining scripts, and put the whole suite behind a CI gate.
Point AI at the repo and answer questions no document can: where a rule really lives, what a status actually means, what a pull request changes for the business.
Use AI to write the SQL you can verify, define metrics that survive scrutiny, build the dashboard nobody had time for, and automate the weekly delivery pack.
Point an AI assistant at your Obsidian vault so fourteen months of notes answer questions directly, and structure the vault so the answers are traceable and correct.
The write side of an AI connection: what to automate, what never to, the approval pattern, the dedicated account, and how to keep generated tickets owned by a human.
How a camt.053 is structured: balance types, entries, entry details, bank transaction codes, and the references that let you match a statement line to a payment.
What the Model Context Protocol is, how to connect an assistant to Jira and Confluence safely, and the six read-only questions that pay for the setup in a week.
Why analysts who send, chain, and script API requests move faster: verified requirements, reproducible defects, faster triage, credible POCs, and a 30-day plan.
How an analyst builds an API proof of concept: the decision it must settle, a two-day spike, mocks from OpenAPI, webhook proof, a scripted demo, and evidence.
Chain API requests in Bruno and Postman: capture values, pre-request and post-response scripts, token refresh, polling, branching, and a Stripe sandbox flow.
The OWASP API Security Top 10 (2023) as test cases analysts can run in Bruno or Postman: object and field authorization, auth, limits, business flows, and more.
How to write API test cases from the contract: a six-source derivation method, 40 worked cases for one payment endpoint, and data-driven automation in Bruno.
Run API test collections in CI: Bruno CLI and Newman in GitHub Actions, secrets, tags, JUnit and HTML reports, private networks, and flaky-test rules.
Build a first API collection in Bruno and Postman: environments, variable precedence, inherited auth, secrets in .env or a vault, and requests imported from cURL.
GraphQL for analysts: queries, variables, mutations, errors returned with HTTP 200, cursor pagination, and hands-on testing of the GitHub GraphQL API in Bruno.
How webhooks work and what analysts must specify: events, signatures, retries, duplicates, and ordering, plus testing with webhook.site and the Stripe CLI.
What an API is and how one works, for analysts: request and response, methods, headers, auth, status codes, and a real GitHub API call you can send today.
Troubleshoot failed API requests by symptom: connection and SSL errors, 401 vs 403, wrong-URL 404s, 415 and 422, 429, 5xx, CORS, and unresolved variables.
Small businesses now need SEO and GEO: ranking in Google and being quotable by AI answer engines. The shared technical base, what GEO adds, and how to measure.
When an AI agent beats a single prompt for analyst work, what tools it needs, where the guardrails go, and the three agent flows worth building first.
The five AI flows that measurably speed up analyst delivery: transcript to draft spec, negative test matrix, test data, log triage, and contract diffs.
Which analyst tasks to automate first, ranked by payback: environment checks, test data setup, reconciliation, contract validation, ticket evidence.
How to build a second brain as a technical analyst: a plain-Markdown knowledge base, organized by system and decision, that answers questions months later.
Developers do not need you to read code. They need decisions, edge cases answered before they hit them, and the why behind the what. The questions that work.
The difference between an API key, a personal access token, and an OAuth token, how to scope and rotate them, and where they belong across an analyst toolchain.
Use a personal access token and the Jira and Confluence REST APIs to generate traceability matrices and publish specs, with working Python scripts.
Bruno stores API collections as plain files in your repo; Postman stores them in a cloud workspace. The trade-offs, with a pacs.008 test suite in both.
A Claude Skill packages your method, references, and scripts into a folder the model loads on demand. Build one that writes pacs.008 test cases.
Migration programmes scope the payment rail and forget everything reading it: screening, monitoring, the warehouse, reports. The downstream impact register.
Namespaces, element order, the business header, empty vs absent, amount precision, and code choices. The XML layer that fails messages your test tool accepts.
How to build an Obsidian vault for analyst work: folder structure, atomic notes per ISO 20022 message and reason code, daily investigation logs, and Jira sync.
What slowly changing dimensions are, how SCD Type 1 and Type 2 differ, how a Type 2 table works with valid_from and valid_to, and how to query it correctly.
Smoke testing proves the build is testable, sanity testing proves a fix landed, regression testing proves nothing else broke. How the three differ and when each runs.
What SQL window functions do and the three patterns analysts use daily: ROW_NUMBER for latest record per group, LAG for event timing, SUM OVER for running totals.
Airflow schedules tasks; Dagster declares the data assets those tasks produce. What the task vs asset split means for lineage, testing, and debugging pipelines.
A business analyst describes intended behavior; a technical BA verifies it against the running system. What separates the roles and how to cross the gap.
Data engineers build the pipelines and platforms that move data; analytics engineers model it in the warehouse so it means something. The split, explained.
A data mesh decentralizes data: domains own and publish their data as products, on a self-serve platform, under federated governance. Explained with a diagram.
A dbt DAG is the dependency graph of your transformations, built from ref() calls. How to read one, the staging to marts convention, and why tests live on nodes.
dbt and SQLMesh both turn SQL into tested, versioned transformation pipelines. Where they differ: SQL parsing, environments, incremental state, and lineage.
Both write code against the same systems. The backend developer owns the production system; the developer analyst uses code to verify and automate analysis.
Fivetran and Airbyte both move source data into your warehouse. They differ on openness, hosting, pricing, and who fixes the connector when the API changes.
Kimball builds dimensional marts first and integrates through conformed dimensions; Inmon builds a normalized enterprise warehouse first. The classic debate, mapped.
A warehouse is a managed analytical database; a lakehouse adds warehouse guarantees to open files on object storage. The real differences, with a diagram.
Looker is a code-defined semantic layer that governs metrics; Tableau is the strongest visual exploration tool. What each optimizes for and when each fits.
The medallion architecture organizes a lakehouse into bronze (raw), silver (cleaned), and gold (business-ready) layers. What each layer owns, with a diagram.
Medallion and Data Vault answer different questions: medallion says how many quality layers, Data Vault says how to model history inside them. With diagrams.
A QA analyst derives what must be tested from requirements and risk; a test engineer builds the automation that runs it at scale. The split and the overlap.
Snowflake and BigQuery are both elastic cloud warehouses. They differ on compute models, pricing units, cloud lock-in, and the knobs your team must operate.
Star and snowflake schemas differ in one thing: whether dimensions are denormalized. What each looks like, when each fits, and how to read one. With diagrams.
A systems analyst maps how systems connect and behave; a solutions architect decides how they should, and answers for it. The line, the overlap, and the jump.
A self-assessment matrix of 25 skills across the five technical analyst hats, with three levels per skill and where to build each one. Original to this site.
Both are technical; the deliverables differ. A technical BA ships verified understanding and specs; an engineer ships production code. Which seat fits you.
A practitioner guide to API testing: status codes, response schemas, request chaining, authentication, error contracts, and the checks that actually catch defects.
What contract testing is, how it differs from integration testing, and how consumer-driven contracts catch breaking API and event changes before they reach production.
What a data dictionary is, how to build one, and why a single authoritative definition of every field prevents the ambiguity that breaks integrations and reports.
Field notes on going from intimidated to fluent with OpenAPI: what confused me, what clicked, and how reading the contract myself changed how I work as an analyst.
The Git an analyst actually needs: clone, navigate, read a diff, browse history, and find when behavior changed. Read the codebase without breaking anything.
An analyst's guide to HTTP status codes: the 2xx, 4xx, and 5xx families, what each common code means, and why 202 vs 200 matters in payments. Practical, not exhaustive.
How to test idempotency in APIs and event consumers: idempotency keys, duplicate requests, redelivered events, and the race conditions that cause double processing.
The integration patterns that wire systems together: request-response, messaging, publish-subscribe, request-reply, batch file transfer, and webhooks. With payments examples.
How an analyst reads JSON: objects, arrays, nesting, and types. Understand API payloads, event messages, and config without asking a developer. Practical, not theory.
A practitioner guide to testing Kafka: consuming events in a test, asserting schema and key, verifying ordering, duplicates, and the consumer side effects that matter.
The production support skills that make a technical analyst invaluable: triage, tracing transactions, reading logs, calm under pressure, and turning incidents into requirements.
How an analyst reads an API contract: endpoints, methods, request and response schemas, status codes, and OpenAPI structure. Understand any API without asking a developer.
Field notes from a major payments incident: how reading logs under pressure works, what the trail revealed, and the lessons about observability that became requirements.
How an analyst reads production logs to understand and debug a system: correlation ids, log levels, searching by transaction, and following one request across services.
The regular expressions an analyst actually needs: matching patterns in logs, validating formats like IBAN and BIC, and searching data. Practical regex, not theory.
How to do regression testing in payment systems: what to retest, building a regression suite, risk-based selection, and automating the checks that protect live behavior.
How an analyst uses Python to automate repetitive checks: calling APIs, comparing files, querying data, and chaining requests. Small scripts, large leverage.
The SQL a technical analyst actually needs: SELECT, WHERE, JOIN, GROUP BY, and reading state during analysis and testing. Not for reports, for finding the truth.
Synchronous and asynchronous communication differ in whether the caller waits, and that choice shapes coupling, latency, resilience, and the customer experience.
A developer analyst reads and writes enough code to verify, automate, and prototype, without becoming a full-time engineer. Here is what the role does in payments.
A QA analyst proves a system behaves correctly by testing it end to end, not by reading the spec. Here is what the role does in banking and payments.
A systems analyst maps how services, messages, and data flow across a system so the pieces work as a whole. Here is what the role does in banking and payments.
Why testing a payment flow end to end teaches you the system and the UX better than any diagram: microservices, Kafka, the database, logs, and pain.002.
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