JJ LOWERY.

Systems Analyst · Business Systems Analyst

Tucson, Arizona · Remote

JJ LOWERY.

I turn messy processes and disconnected information into clear, reliable systems.

I analyze how work, data, and software actually behave—then translate that understanding into better workflows, explicit business rules, automation, decision-support systems, and verifiable improvements.

View My Work View Résumé
Human perspective.
Systems thinking.
Professional portrait of James “JJ” Lowery
James “JJ” LoweryThe person behind the process
Business context. Technical depth.Investigate → Model → VerifyExplore the work

01 / Selected systems work

The thinking. The work. The proof.

Three systems problems. A closer look at the investigation, the model, and the evidence behind each solution.

Case study 01Boldly Grow Media

Decision-support systems

Advertising Budget Planning & Pacing System

From repetitive data collection to an operational decision-support system.

A Google Sheets-based operating system connecting monthly budget planning, actual spend, projections, and corrective recommendations across a multi-client portfolio.

Read case study : Advertising Budget Planning & Pacing System
Budget control loop
InputBudget + strategy
  1. 01Plan
  2. 02Observe
  3. 03Project
  4. 04Diagnose
  5. 05Act

Forward planning + continuous feedback

Case study 02Vector

Domain modeling & requirements

Redesigning Income Planning Around Real Cash Flow

When a UI problem turned out to be a domain-modeling problem.

Separating actual income, expectations, planning intent, and funding to define financial behavior that stays consistent with reality.

Read case study : Redesigning Income Planning Around Real Cash Flow
One system. Four distinct concepts.
01Reality
What happened
02Expectation
What may happen
03Planning intent
Which month it supports
04Funding
What backs the plan

Related concepts ≠ interchangeable concepts

Case study 03Vector

Root-cause analysis & validation

Diagnosing a Production-Scale Performance Failure

A feature that worked at test scale failed when real production history activated the full read path.

A production-shaped reproduction isolated an expensive database helper. A minimal redesign reduced measured helper time while preserving existing behavior.

Read case study : Diagnosing a Production-Scale Performance Failure
Measured helper execution time
Before11,136.6 ms
After41.3 ms
~99.6% reduction~270× faster

1,004-row synthetic reproduction · semantics preserved

02 / Capabilities

From ambiguity to something that works.

A practical toolkit for understanding systems and making them more reliable.

01

Requirements & Business Rules

Turn ambiguity into explicit requirements, decision rules, edge cases, and acceptance criteria that people can review and test.

02

Process & Workflow Analysis

Trace how work actually moves. Identify handoffs, repeated effort, and failure points before designing a clearer future state.

03

Systems & Data

Separate concepts that behave differently, map how information connects, and build structures that support reliable decisions.

04

Root-Cause Analysis

Move from symptoms to evidence. Reproduce the conditions, isolate the bottleneck, and test the explanation.

05

Automation

Structure repeatable work, define the conditions for action, and keep human judgment at the points where it matters.

06

Testing & Validation

Check intended behavior, failure cases, and regressions. Verify that the improvement works without breaking existing rules.

03 / How I approach systems problems

Make the system understandable before making it automatic.

A working principle

Good systems start with better questions. Then the model, the implementation, and the evidence have to agree.

  1. 01

    Investigate

    Observe the current state. Follow the work and the evidence.

  2. 02

    Model

    Separate the concepts. Make relationships and constraints visible.

  3. 03

    Design

    Define the future state, business rules, and acceptance criteria.

  4. 04

    Implement

    Translate the model into workflows, tools, or application behavior.

  5. 05

    Test

    Exercise the rules, edge cases, and conditions that caused failure.

  6. 06

    Verify

    Compare the result with the intent. Confirm the improvement holds.

The purpose of structure

Structure the repeatable work so human expertise can be spent where judgment actually matters.

04 / Supporting systems work

The same lens. Different systems.

MagMod01

EOS / Level 10 Operating System Implementation

Helped implement EOS across the company and operationalized the Level 10 meeting process for executive leadership and the marketing organization. Led recurring executive L10 meetings, maintained supporting operating documents, tracked issues and commitments, and facilitated the structured process used to surface, prioritize, discuss, and resolve organizational problems.

Organizational operations

Boldly Grow Media02

Monthly Reporting Standardization

Standardized recurring client performance reporting into a predictable structure with defined places for data, interpretation, and recommendations, reducing ad hoc production and making routine reporting more delegable.

Process standardization

Vector03

Safe Transaction Automation

Observed pattern ≠ suggested automation ≠ authorized automation ≠ successful future execution.

Automation requires current proof; ambiguous or conflicting evidence fails closed. Durable claims, events, and receipts prevent duplicate execution. Bank Activity is source evidence, the Ledger is the approved financial record, and standing automation is deliberate authorization.

Rules & authorization

Vector04

Production Migration Discipline

Production schema changes are controlled state transitions, not just code deployments.

Repository state ≠ database state. Staging success ≠ Production safety. Application version ≠ database version. These distinctions frame application change validation and the evidence needed before a production change.

Change validation

Boldly Grow Media05

Browser Workflow Automation

Used browser-based JavaScript automation to reduce repetitive Google Ads campaign setup work involving hundreds of manual selections. Saved roughly 30–45 minutes per applicable campaign build.

Workflow automation

05 / Experience

Business experience.
Systems perspective.

View full résumé
  1. 2024–2025

    Waypoint Media Co.

    Co-Founder & CEO

  2. 2021–2024

    Boldly Grow Media

    VP, Performance & Connected Media

  3. 2017–2020

    MagMod

    Marketing Director / Executive Leadership Team

06 / About

I’ve always been interested in the system underneath the work.

My career began in marketing and business leadership, but the work I repeatedly gravitated toward was the system underneath the work: how data was collected, how decisions were made, how teams coordinated, how repetitive processes could be automated, how business rules should behave, and why a workflow or application was failing.

Today I bring that same approach to systems analysis: investigate the current state, identify constraints and failure points, model the desired behavior, define rules clearly, help implement the improvement, and verify that the result actually works.

James “JJ” Lowery Tucson, Arizona

Let’s connect

Have a system that needs untangling?

I’m pursuing remote Systems Analyst, Business Systems Analyst, Applications Analyst, and related systems-focused opportunities.

Connect on LinkedIn (opens in a new tab)