BI Interviews in the AI Era, GitHub's Privacy Grab, and the Jobs AI Will Actually Take

30 BI Engineering Interview Questions That Actually Matter in the AI Era

TLDR: The standard BI interview is stuck in 2022. Anusha Kovi, a data and BI engineer focused on governed AI for data platforms, argues that half the questions being asked today are flat-out irrelevant, and she's written the ones she'd actually use instead.

Summary: There's a specific kind of frustration that comes from sitting across the table from someone in 2026 and being asked to write a basic SQL window function by hand. Not because it's hard, but because it signals that the interviewer hasn't updated their mental model of what the job actually is. Anusha Kovi went through that experience in 2022, and she's watched the gap between interview practice and real-world BI work grow wider every year since.

The piece lays out 30 questions designed around what modern BI engineering actually demands. We're talking about governance of natural language query interfaces, handling the trust layer between an AI-generated insight and a business decision maker, and understanding where semantic layers break down under load. These are not theoretical concerns. They're the problems BI engineers deal with every week in 2026.

What I find genuinely useful here is the shift in framing. Old-school BI interviews tested whether you could wrangle data. The questions Kovi proposes test whether you understand the full accountability chain when an AI tells a VP of Sales that revenue is up 12% and it's wrong. That's a completely different skill set, and most interview processes haven't caught up.

The sample question she opens with is a good litmus test: write a query to find the second-highest revenue day per product category in the last 90 days. That's still fair. But it's the questions she adds around it that matter. What happens when the AI assistant generates that query incorrectly and nobody catches it? That's where 2026 BI engineering lives.

Key takeaways:

• Interview questions should reflect the reality that NL-to-SQL and AI-assisted analytics are now part of daily BI work
• Governance, trust, and accountability layers matter as much as query proficiency
• The best questions probe how candidates handle AI-generated errors upstream of business decisions
• Most companies are still hiring for 2020 BI skills and wondering why their AI data products fail

Why do I care: From an architecture standpoint, this is directly relevant to anyone building data products. The same governance gaps that make BI interviews outdated are the ones that cause AI-generated dashboards to silently mislead entire organizations. I'd add this to any senior data platform design conversation.

30 BI Engineering Interview Questions That Actually Matter in the AI Era

GitHub Wants Your Private Code to Train AI. What's Your Move?

TLDR: Starting April 24, GitHub will use Copilot interaction data, including code from private repositories, to train AI models by default. The same week, Copilot was caught injecting ads into over 1.5 million pull requests.

Summary: Let's be clear about what's happening here. GitHub announced a policy change that opts you into sharing your private code for AI training unless you actively turn it off. The timing is not great, because this announcement landed in the same week someone discovered Copilot had been injecting sponsored content into pull request suggestions at scale. One point five million pull requests. That's not a bug, that's a product decision dressed up as a bug.

The HackerNoon community poll results are actually illuminating. Twenty-seven percent of respondents said every platform does this eventually anyway, which is either cynical wisdom or learned helplessness depending on your mood. Twenty-one percent have already opted out and are actively looking at self-hosted alternatives like Codeberg. Eighteen percent are opting out but staying on GitHub, which is probably the most common response among working developers who can't realistically migrate their entire workflow overnight.

What strikes me is how the trust erosion compounds. The code training policy alone would be a significant conversation. But paired with the ad injection incident, it creates a pattern where GitHub as a product is drifting away from developers and toward monetization. That's not inherently surprising for a Microsoft-owned platform, but it's the pace that's jarring. These two things happened in the same week.

The practical question is whether opt-out is enough. If you're a developer working on proprietary software, especially anything with sensitive business logic or security-adjacent code, you need to verify your organization's GitHub settings before April 24. The default is not in your favor.

Key takeaways:

• GitHub defaults to using your private repo code for AI training starting April 24; you must opt out explicitly
• The same week, Copilot was found injecting ads into 1.5M+ pull requests, significantly damaging trust
• Community response is mixed: about a fifth are considering self-hosting, most are grumbling but staying
• Organizations with sensitive codebases should audit their GitHub settings before the deadline

Why do I care: This is an architecture and security concern, not just a privacy concern. If your team is using Copilot in enterprise repos and you haven't talked about this, that's a gap in your threat model. The opt-out is trivial to do. Knowing you need to do it is the harder part.

Poll: GitHub Wants Your Private Code to Train AI. What's Your Move?

Microsoft Generative AI Report: The 40 Most Disrupted Jobs and The 40 Most Secure Jobs

TLDR: Microsoft Research analyzed 200,000 real-world Copilot interactions to produce an empirical list of which professions are most exposed to generative AI disruption, and which ones aren't. The methodology actually matters here, because it's data from real usage, not speculation.

Summary: There's no shortage of "AI will take your job" articles, most of which are based on vibes or GPT-4 capability benchmarks that don't reflect how people actually use these tools day to day. This Microsoft report takes a different approach. It's derived from 200,000 actual Copilot interactions across real organizations, which means the disruption scores are grounded in observed behavior rather than theoretical capability.

The core finding is that the jobs most at risk are the ones where the work is primarily about producing structured outputs from well-defined inputs. Think report writing, data entry, basic code generation, content templating. These are tasks where a language model can substitute for a human in a way that's measurable and repeatable. The disruption isn't about AI being smarter than humans; it's about AI being fast enough and accurate enough on narrow, repeatable tasks that hiring a human to do them stops making economic sense.

The secure end of the list is more interesting to me. Physical trades, roles requiring real-time judgment in unpredictable environments, and anything involving direct human accountability under legal or ethical frameworks tend to score well. A plumber and a judge both show up in safer territory, for very different reasons, but the pattern holds.

For developers and architects, the uncomfortable data point is that basic code generation is already in the disrupted category. What isn't disrupted is system design, debugging complex distributed systems, and the kind of judgment calls that require understanding organizational context. That's where the value is moving, and it's worth being honest about the shift.

Key takeaways:

• The study used 200,000 real Copilot interactions, making it more grounded than theoretical capability assessments
• Most disrupted: roles producing structured outputs from defined inputs (reports, templates, basic code, data entry)
• Most secure: physical trades, roles with legal accountability, complex judgment in unpredictable environments
• For developers: basic code generation is disrupted; system design and architectural judgment are not

Why do I care: This is the kind of data I want in every conversation about AI and developer roles. Anecdotes about what Copilot can or can't do are less useful than empirical data on where it's actually being substituted for human work. Worth reading before your next org chart conversation.

Microsoft Generative AI Report: The 40 Most Disrupted Jobs and The 40 Most Secure Jobs

HackerNoon Projects of the Week: Movement Network Foundation, Packworks and Kyram

TLDR: HackerNoon's Proof of Usefulness Hackathon spotlights three projects this week: a modular Move-Ethereum framework, a retail operations platform, and a social fitness app. The hackathon scores on real-world utility, not pitch deck promise.

Summary: I'll be honest, hackathon roundups aren't usually where I find the most interesting ideas. But HackerNoon's framing for the Proof of Usefulness competition is worth noting. The explicit scoring criteria is real utility over hype, which is a direct response to the pitch deck theater that dominated Web3 and early AI startup culture. Whether they actually deliver on that framing is a separate question, but the intention is pointed in the right direction.

Movement Network Foundation is building a modular framework that bridges Move-based smart contract development with Ethereum's ecosystem. The technical premise is interesting: Move was designed with resource-oriented programming that makes certain classes of smart contract bugs structurally impossible, and bringing that to Ethereum's network effects is a reasonable bet if the interoperability holds up in production. Their Proof of Usefulness score came in at -5 out of 1000, which, I'll be honest, is a confusing metric to lead with in a promotional piece.

Packworks targets retail operations, specifically small and medium retailers dealing with inventory and logistics complexity. That's a real problem, and the B2B SaaS angle is more grounded than most blockchain plays. Kyram is in the social fitness space, which is extremely crowded, but the community-driven angle differentiates it at least in concept.

The broader point is that the Proof of Usefulness Hackathon exists because the industry recognized it kept funding impressive demos that didn't survive contact with real users. Applying that same skepticism to your own side projects or internal tools is a habit worth building.

Key takeaways:

• HackerNoon's hackathon explicitly scores on utility, not pitch quality
• Movement Network Foundation bridges Move-language smart contracts with Ethereum's ecosystem
• Packworks addresses retail operations logistics for SMBs
• Kyram takes a community approach to social fitness

Why do I care: The "utility over hype" scoring model is something I'd love to see applied to internal enterprise tooling. Most organizations have the equivalent of a hackathon problem every quarter, shipping tools that score well in the demo and fail in production. The discipline of asking "what real problem does this solve for a real user today" should be a standard engineering checkpoint, not just a hackathon rule.

HackerNoon Projects of the Week: Movement Network Foundation, Packworks and Kyram