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Deep Dives
Data Driven
My Passion for AI & Machine Learning: My love for AI ignited when I dove deep into enterprise-grade machine learning and recommender systems. While I didn’t personally build the models, I immersed myself in studying personalized recommendation and Fraud Detection engines for major e‑commerce platforms. This deep exploration lit my fire for Machine Learning, Neural Networks, Deep Learning and, with the ‘coming out’ of AI two years ago, my passion for Prompt Engineering and LLM research.
Core Strengths: I leverage vibe coding, prompt engineering and process improvement to build context‑aware prompts (video above) that guide AI assistants to surface top‑tier candidates, automate stakeholder follow‑ups, and generate interview playbooks on demand. My knack for vibe coding shines through seamless integrations between Lovable.dev, Cursor, GitHub Copilot, Google Apps Script, ChatGPT and integrations with Slack, Google Workspace, Greenhouse, Lever, etc., unlocking self‑healing pipelines that adapt to real‑time feedback. As an ethical AI steward, I lead with an unwavering commitment to transparency, bias mitigation, and GDPR compliance, ensuring AI amplifies fairness and inclusivity at every stage.
Showcase Projects
Tools & Integrations: I build connected, multi‑tenant AI platforms rather than siloed folder systems. I leverage:
Continuous Learning & Industry Engagement: I stay at the forefront of AI advancements by exploring Product Hunt launches, subscribing to leading AI newsletters, engaging in community forums, and experimenting with emerging platforms. This habit ensures my solutions leverage the latest innovations—from early‑access LLM features to niche research tools.
Philosophy & Vision: My mission is to empower recruiters with AI that feels less like a black box and more like a trusted teammate—delivering efficiency without eroding empathy. By combining recursive recruiting with ethical guardrails, I pioneer processes that learn, adapt, and scale while keeping humans at the center.
ML & Infra
Data Hiring Specialist. AI-Enhanced. Recursive by Design.
From massive ETL migrations to real-time stream processing and AI-enhanced pipelines, I’ve led recruiting for Data Engineering orgs that power everything from diagnostics to national home search platforms. My background spans both pre-IPO scaling and post-IPO stabilization. Whether it’s building resilient cloud-native infrastructure or wrangling messy org-wide intake processes, my approach is recursive—designed to improve signal, feedback loops, and stakeholder clarity quickly over time.
Data Experience by Company
🚰 Principal Software Engineer, Data Platform 2.0 – Realtor.com
Roles Supported: Principal Software Engineer, Data Engineers, Staff Engineers
Technologies: AWS, Kafka, real-time processing, Snowflake, dbt, CI/CD
Realtor.com was re-architecting their legacy data platform into a modern, scalable stack with real-time and batch processing. This wasn’t just a migration—it was a trust reset between Engineering, Product, and the Data org.
Challenge:
Stakeholder alignment was scattered. No unified signal from Product, Data Science, and Engineering. The hiring manager’s expectations were evolving week-to-week as architecture decisions changed.
How I Solved It:
I created a living intake dashboard and initiated recursive recruiting loops—where every debrief and stakeholder convo was fed back into the role’s core docs, JD, sourcing tools, and scorecards. This approach clarified the delta between “dream” and “hire-ready” and improved time-to-signal.
Result:
Finalist was hired and immediately redesigned a key ingestion pipeline—cutting event lag from 6 hours to 30 seconds. Engineering called it “the cleanest onboarding we’ve ever had at this level.”
🧬 Staff Data Engineer & Platform Roles – GRAIL
Roles Supported: Staff Data Engineers, Data Infrastructure, Bioinformatics Engineers
Tech Environment: GCP, BigQuery, Airflow, Kubernetes, HIPAA-aligned infra
GRAIL’s Data Engineering team sat at the core of a multi-modal, billion-row diagnostics platform. Their job? Make clinical and genomic data not just available—but fast, compliant, and extensible.
Challenge:
Most available data talent had fintech or retail backgrounds—few understood the intersection of regulated infra and cloud-native pipelines.
How I Solved It:
I worked directly with Engineering and Regulatory teams to define what “transferable compliance experience” looked like. From there, I built a mapping doc that translated tools like dbt, Terraform, and GCP IAM roles into risk-aligned maturity signals for the business.
Result:
New hire led the migration from Cloud Composer to a hybrid Airflow/Kubernetes stack—improving reliability by 30% and decreasing compute spend by 18%.
🛍️ Director of Data Science, Recommender Systems – Shopify
Roles Supported: Director of Data Science (Recommendations Team)
Tech Environment: Python, TensorFlow, Kubernetes, Real-Time Personalization Engines
Shopify urgently needed a Director of Data Science to lead the recommender systems powering its Black Friday rollout—a critical revenue driver. This was a backfill made even more high-stakes by the immovable Black Friday deadline just six weeks away.
Challenge:
The candidate needed to not only lead model deployment for a brand-new personalization engine but also stabilize a team that had just lost its leader mid-project.
How I Solved It:
I structured an aggressive but candidate-friendly campaign using personalized outreach referencing Shopify’s real-time personalization roadmap and the sheer impact potential for the candidate. I ran tight intake loops every 48 hours with Engineering, PMs, and Data stakeholders to course-correct outreach as needed. I also leveraged Shopify’s brand equity but reframed it through a builder’s lens: pitching this as a “tech founder moment inside an iconic brand.”
Challenge:
Competitors (like Meta, Amazon, and ByteDance) were courting the same talent pool—and offering bigger paychecks.
How I Solved It:
I targeted candidates motivated by impact velocity, team leadership, and the chance to ship a system with hundreds of millions of immediate impressions. I sourced primarily from alumni of personalization-heavy companies like Pinterest, Netflix, and Spotify, focusing on those who had launched new ranking or recommendation features in under six months.
Failure:
Early candidates skewed too theoretical—academically brilliant, but not delivery-optimized. I refined my screen to prioritize "productization under deadline" examples.
Search Strategy:
("recommendation system" OR "recommender" OR "ranking models" OR "personalization") AND ("data science director" OR "data science lead") AND ("e-commerce" OR "retail technology") AND ("real-time" OR "production ML systems")
Result:
Sourced, attracted, closed, and onboarded the finalist three weeks before Black Friday. They immediately led stabilization of the real-time ranking models, hitting key KPIs for engagement lift. The rollout went off successfully—and the team credited their leadership with saving the initiative.
📱 Data Adjacent Engineering Roles – MobileIron
Roles Supported: Data Security Engineers, MDM Infra, Threat Intel Pipelines
Tech Environment: iOS/Android SDKs, on-device telemetry, secure tunneling
While technically part of Security, MobileIron’s work on mobile telemetry pipelines was a Data Engineering challenge at its core.
Challenge:
Most candidates hadn’t built for mobile—and even fewer understood secure logging and event routing from devices to cloud.
How I Solved It:
I filtered for contributors to OWASP Mobile and developers active in AppConfig Community, then layered in search criteria for ingestion, queuing, and telemetry.
Result:
The new hire helped refactor the mobile threat defense pipeline to eliminate a 400ms delay in alerting—a move that allowed for real-time MDM enforcement on compromised devices.
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