Interview Prep — Nick Haylund (phData)

Conversation is tomorrow. This is the Nick-specific note. For company/role background, see 01-projects/phdata/interview-prep-round3.

Quick Reference: Nick Haylund

Why this conversation matters: Nick owns the practice you’d be working alongside. He’ll be assessing whether you understand analytics at the practitioner level, not just the architecture level. Match his technical depth when it comes up, then pivot to the business value layer.

Conversation Flow — 5 Angles

1. Alteryx ACE Connection (Icebreaker)

The ask: Nick is an Alteryx ACE. Andrew (who you know at phData) is also an Alteryx ACE. Did they know each other from the Alteryx community before phData?

Why it matters: The Alteryx ACE network is small and genuinely close-knit. If they knew each other pre-phData, that’s a revealing data point about how phData’s analytics department was assembled — community first, not just acquisition. It also signals that you’ve done your homework and understand that the analytics culture here was built by practitioners, not hired from a slide deck.

How to open it: “I noticed you and Andrew are both Alteryx ACEs — was that connection there before phData, or did it develop after the Tessellation deal?”

Your bridge: You’ve worked with the Alteryx stack from the analytics engineering side — you understand what ACE-level mastery looks like and why someone pursues it.

2. Netherlands / Amsterdam

The ask: Check if Nick spent time in the Netherlands. You spent ~six months in Amsterdam building TagaPet through a startup accelerator.

Why it matters: International experience creates instant rapport when it aligns. Amsterdam specifically has a distinct startup culture — open, pragmatic, builder-focused. If Nick spent time there, it’s a genuine shared reference point, not small talk.

How to open it: Mention your Amsterdam chapter naturally — “I did a startup accelerator out of Amsterdam for about six months building TagaPet, a pet health platform. I noticed from your background you may have had some time in Europe — did you spend any time in the Netherlands?”

If he didn’t: Drop it cleanly. Don’t over-explain. The Ireland study-abroad connection (Loras College) is a valid fallback — you can mention that you also have some international thread in your background.

3. Tessellation → phData: The Acquisition Story

The ask: Understand what the transition from Tessellation to phData actually felt like — and what’s changed since.

Questions to explore:

• “How has the analytics department evolved since the Tessellation acquisition? Were you building the practice from scratch inside phData, or did you inherit existing analytics engagements?”
• “What’s surprised you about phData’s growth over the last few years — anything that looked different from the inside versus what you expected?”
• “What keeps you excited about coming to work every day at this point?”

Why it matters: You’re evaluating culture as much as they’re evaluating you. Nick is someone who joined via acquisition — he’s been inside the org long enough to have a real opinion. His answer to “what keeps you excited” is a signal about leadership trust, client quality, and whether the analytics practice is being invested in or just maintained. If he hesitates, that’s information too.

Your frame: You’ve lived through a version of this — building inside a PE-backed company (ConnectWise / Thoma Bravo) and watching priorities shift as the acquisition roadmap evolved. That’s a credible parallel.

4. Remote vs. HQ / Travel Expectations

The ask: Nick is in Minneapolis — does he go into HQ or work remote? Natural segue into travel expectations for the role.

How to open it: “Since you’re in Minneapolis and phData is headquartered there — do you find yourself going into an office most days, or is the team largely distributed even from HQ?”

Follow-up: “For the analytics and visualization engagements, is there typically a meaningful on-site component with clients, or is most of the delivery remote?”

Why it matters: The role is listed as remote, but consulting always has travel embedded in it somewhere — client kickoffs, QBRs, on-site workshops. Understanding what Nick’s team actually does (vs. what the job description says) gives you the real picture. You can travel and want to, but you need to know the realistic cadence to plan around Ray Data Co.

5. Department Vision & Sample Engagements

The ask: Understand the analytics practice’s vision and what kinds of work the team actually does — no client names needed.

Questions to explore:

• “What’s the vision for the analytics and visualization practice over the next 12-18 months — where are you trying to take it?”
• “Could you walk me through a sample engagement? Even anonymized — what does a typical analytics project look like end-to-end, from scoping through delivery?”
• “What domains do you tend to see the most analytics work in — financial services, manufacturing, retail? Does the type of analytics problem vary much by vertical?”

Why it matters — your framing: Your analytics background at Mammoth Growth skewed heavily toward growth marketing — multi-touch attribution, retention modeling, segmentation, funnel analytics for SaaS and e-commerce. That’s powerful work, but it’s a specific lens. You want to understand how phData’s analytics engagements differ: are they doing operational analytics, financial reporting, supply chain visibility, BI modernization? That tells you how much of what you know directly transfers and where you’d be learning new domains fast.

Be ready to share your angle: “Most of my analytics work at Mammoth has been in growth marketing — MTA, retention, segmentation. I’m curious how that maps to what your team typically sees. Are those patterns, or is it mostly different terrain?”

STAR Stories — Analytics & Visualization Focus

Story A: Net New MRR as Organizational Operating System (ConnectWise)

Situation: 30-year-old tech company with a fragmented “zoo” of products — no shared metric, no shared data language. Teams optimized locally, leadership flew blind on the number that actually mattered.

Task: Design and deploy a single unifying analytics layer that every department could see themselves in.

Action: Selected Net New MRR (Land + Expand - Downgrade - Churn) as the north-star metric. Integrated seven production systems to produce it reliably. Built dashboards that functioned as business scoreboards — not just reporting artifacts. Ran an internal roadshow: monthly all-hands, lunch-and-learns, department-level meetings to teach the metric. Sales restructured into hunter/farmer teams based on the new definition. Support launched proactive churn-buster initiatives aligned to the downgrade/churn component.

Result: Leadership aligned bonuses and equity to MRR growth. During acquisition due diligence, the analytics team’s ability to trace everything to a single metric gave buyers confidence. Company closed at unicorn valuation; approximately 70 employees became millionaires.

Use when: Nick asks about analytics impact at scale, stakeholder alignment, or what good visualization work enables at the business level.

Story B: Visualization as Governance (ConnectWise Deployment Process)

Situation: Growing analytics team, multiple analysts committing to a shared production Power BI environment. No process for managing changes — anyone could break anyone else’s reports.

Task: Design a deployment process that protected report quality without slowing delivery.

Action: Built a PR-based deployment workflow where every change to the dbt transformation layer required both a technical peer review and a Data Governance review for business logic impact. Power BI dashboards were downstream of a stable, version-controlled model layer — you couldn’t ship a broken report if the data model review held. Documented the entire process as an onboarding guide so new analysts could be productive in days, not weeks.

Result: Eliminated the “why are the numbers different?” fire drills. The process became the standard for all ConnectWise analytics development. Directly deployable as a template for any phData client engagement.

Use when: Nick asks about analytics team process, visualization governance, or how you operationalize quality in a multi-analyst environment.

Story C: Multi-Client Analytics Architecture at Mammoth Growth

Situation: As a 1099 Technical Architect, context-switching across multiple client stacks simultaneously — each at a different analytics maturity level, each with different tooling, different business domains.

Task: Deliver high-quality analytics architecture across all clients without letting any single engagement become the only engagement.

Action: Developed a personal pattern library — transformation layer templates, data model conventions, stakeholder communication frameworks — that could be adapted across clients regardless of stack. Observed that most enterprise clients self-assess at L3 analytics maturity but are actually at L1-L2. Made honest maturity diagnosis a repeatable first step in every engagement, followed by a credible roadmap to the next level.

Result: Consistent delivery across parallel engagements. The maturity assessment framework became reusable across every new client — now it’s something that can be deployed in week one of any phData engagement as a diagnostic tool.

Use when: Nick asks about breadth of analytics experience, managing multiple client relationships, or consulting methodology.

Closing Questions (Pick 2-3)

1. On the analytics practice’s differentiation: “How does phData’s analytics practice differentiate from what a client might get from a generalist SI — what’s the thing you do that they can’t replicate?”

2. On the Tessellation legacy: “When you think about what Tessellation brought into phData versus what phData has given back to the analytics practice, what’s the exchange that’s mattered most?”

3. On what he looks for in analytics talent: “When you’re evaluating someone for the analytics team, what’s the thing that separates a good hire from a great one — is it technical depth, communication, business instinct, something else?”

Things to Watch For

• If he goes deep on Alteryx/Sigma/Dataiku: Match the conversation but don’t over-claim fluency you don’t have. The honest answer is “I’ve worked alongside those tools from the architecture side, but my hands-on experience is heavier in dbt and Power BI — I’d be learning the Sigma layer from practitioners like you.”
• If he asks about your analytics background: Lead with the Net New MRR / unicorn exit story. That’s the credibility anchor. Then bridge to Mammoth Growth for breadth.
• If the conversation is short: Prioritize angles 3 (Tessellation) and 5 (department vision). Those give you the most signal about fit.
• Tone: Nick is a practitioner who became a leader. Don’t pitch him. Have a conversation. Ask real questions. He’ll notice the difference.