
Hi D***e, I’m J***e from A***x. I’ve been reviewing your work leading product strategy at U***y and I***5,your depth in programmatic ad tech, identity resolution and viewability really stands out. We’re looking for a Director of Product Management to own our programmatic advertising platform roadmap, and I believe your background could be a great fit. Would you be open to a brief chat to explore this role further?
Hi J***e, thanks for reaching out , I’m keen to learn more about the Director of Product Management role. With my background in programmatic ad tech, identity resolution and viewability at U***y and I***5, I think I could be a great fit. When’s a good time to talk?


Hi D***e,
Great to hear you’re interested. Here’s a bit more on the opportunity and why it could be a strong next step given your background:
1. Company & Role Overview
, A***x is a fast-growing ad tech firm specializing in programmatic platforms and identity solutions.
, As Director of Product Management, you’ll own the end-to-end product vision and roadmap for our programmatic advertising suite, with a strong focus on identity resolution and viewability measurement,areas where you’ve led successful initiatives at U***y and I***5.
2. Key Responsibilities
, Lead cross-functional teams (engineering, design, data science) to deliver scalable, high-performance ad tech products.
, Collaborate closely with sales, marketing and client success to surface market opportunities and translate customer needs into product enhancements.
, Mentor and develop junior PMs, instill agile best practices, and drive on-time delivery of major milestones.
, Analyze product metrics, customer feedback and industry trends to continuously optimize performance and inform go-to-market strategies.
3. Why This Role Is a Fit for You
, You’ll leverage your deep expertise in programmatic ad tech, identity resolution and viewability to shape our strategic direction.
, You’ll have real ownership of product strategy and direct impact on a global client base.
, We provide competitive compensation, equity package and a clear path toward senior leadership (VP/Head of Product).
Does this align with what you’re looking for? Are there any aspects you’d like to dig into further?
Regarding a chat, I’m available tomorrow between 9:00,11:00 AM GMT or Thursday from 2:00,4:00 PM GMT. Do either of those slots work for you, or would another time be better?
Looking forward to connecting.
Best regards,
J***e
HR Recruiter, A***x
Thanks J***e, this sounds like a great fit with my experience at U***y and I***5,could you share a bit on the current PM team size and the biggest challenges you’re tackling? Tomorrow at 10 AM GMT works great for me.


Hi D***e,
Great,tomorrow at 10 AM GMT is locked in. I’ll send a calendar invite with a video link shortly.
To give you a sense of the setup: our Product organization today is a lean team of about six PMs covering identity resolution, viewability measurement, data integrations and client insights. We’ve structured it so each PM owns a core pillar but collaborates closely across engineering, design and data science.
The biggest challenges we’re tackling right now are:
1. Scaling our identity graph globally while staying ahead of evolving privacy regulations (GDPR, CCPA and the post-cookie landscape).
2. Delivering real-time, high-accuracy viewability metrics at scale without compromising page performance.
3. Integrating diverse partner data sources and aligning roadmaps across Sales, Marketing and Client Success.
4. Anticipating market shifts,cookieless targeting, contextual signals, identity linkages,and translating those into clear, impactful product priorities.
Given your experience at U***y and I***5, I think you’ll find plenty of opportunity to drive both strategic direction and execution. Looking forward to diving into this in more detail tomorrow.
Talk soon,
J***e
HR Recruiter, A***x
Thanks, that’s super helpful. Quick question,what does your tech stack and architecture look like for scaling the identity graph (deterministic vs. probabilistic matching)? And on viewability, how are you balancing real-time accuracy with minimal performance impact?


Hi D***e,
Great questions,happy to dive in a bit more before tomorrow’s call.
1. Identity Graph
- We run a cloud-native, microservices architecture (containerized on Kubernetes) with Kafka for real-time event streaming and Spark for heavier batch/ML workflows.
- Deterministic matching lives in our graph-DB layer (hashed IDs, first-party tokens), updated continuously via streaming pipelines.
- Probabilistic matching happens in our data-science tier (Spark/Beam jobs) using machine-learning models to link signals when deterministic IDs aren’t available. We then merge results back into the graph with confidence scores, so downstream services can apply the right level of trust.
2. Viewability Measurement
- Our client SDK is ultra-lightweight JavaScript, capturing basic in-browser signals (viewport checks, time-in-view) and sending a minimal payload to our server endpoints.
- Heavy lifting,fraud filtering, advanced layout analysis,runs asynchronously in the cloud, so the page performance hit stays under 50 ms on average.
- We also use adaptive sampling: full-detail metrics on key placements, leaner data on lower-impact slots, which keeps our real-time APIs performant without sacrificing accuracy where it matters most.
Looking forward to walking through this in more detail at 10 AM GMT tomorrow. If there’s anything else you’d like me to prep, just let me know.
Best,
J***e
HR Recruiter, A***x
Thanks J***e,that’s really helpful. Can you share which graph-DB you’re running and how you’ve scaled ingestion throughput, plus what ML models and retraining cadence you use for probabilistic matching? Also curious how you benchmark your JS SDK’s performance across browsers and catch outlier render times in the viewability pipeline.


Hi D***e,
Great questions,here’s a bit more color:
1. Graph-DB & Ingestion
• We run a distributed, cloud-managed property-graph store layered on Kubernetes.
• Ingestion is powered by Kafka topics partitioned by customer and geo-region,each consumer group spins up multiple pods for parallel writes.
• We autoscale those pods based on lag metrics and use backpressure controls in Kafka + micro-batch Spark jobs to smooth spikes.
2. Probabilistic Matching Models & Retraining
• Our ML tier lives in Spark (with Beam for cross-cloud portability). We leverage Spark MLlib pipelines,logistic regression for link probability and random-forest ensembles to boost precision in complex cases.
• Incremental model updates run nightly on fresh event data; we trigger a full retrain of both ranking and calibration layers weekly to incorporate new patterns.
3. JS SDK Benchmarking & Outlier Detection
• We maintain an in-house browser farm (Chrome, Firefox, Safari, Edge) plus BrowserStack for synthetic runs. We script page loads with our SDK in isolation and in real pages, capturing init-to-render timings and Web Vitals.
• In production, the SDK emits microsecond-precision timing marks back to our pipeline. We compute 95th/99th-percentile baselines per browser and country, and flag any render time beyond a dynamic threshold. Anomaly jobs in Spark window these metrics and surface alerts to our SRE and product teams.
Hope this helps,happy to dig deeper on any point tomorrow at 10 AM GMT. Looking forward to our chat!
Best,
J***e
HR Recruiter, A***x
Thanks J***e, that’s super clear. A couple more quick ones: which graph-DB tech are you running (e.g. JanusGraph, Neo4j, Neptune) and how do you handle schema migrations and cross-region consistency? And on the ML side, what’s your feature engineering & labeling workflow, and how do you monitor drift or performance regressions between nightly updates?


Hi D***e,
Happy to dive in:
1. Graph-DB, Schema Migrations & Cross-Region Consistency
• We run Amazon Neptune in a global-cluster setup (multi-AZ writes with read replicas across regions).
• Schema changes are managed via versioned migration scripts (Liquigraph) in our CI/CD pipeline,each change is reviewed, smoke-tested on staging, then rolled out with automatic rollback on failure.
• Neptune’s global replication ensures low-latency reads worldwide and uses a quorum-based approach to keep writes consistent.
2. Feature Engineering & Labeling Workflow
• All feature definitions live in Airflow DAGs that kick off Spark/Beam jobs. Raw event streams land in our Delta Lake, features are materialized nightly into a central feature store.
• Labeling combines deterministic graph-derived links (first-party IDs) with rule-based heuristics; we surface sampled cases to a small QC team for monthly manual validation and bias checks.
3. Drift & Regression Monitoring
• After each nightly retrain, we compute distribution metrics (KS divergence, population stability index) for every feature and track core model metrics (AUC, precision, recall).
• Any metric that drifts beyond pre-set thresholds automatically triggers alerts in Grafana/PagerDuty. We also maintain a fixed holdout set to compare current vs. baseline performance and can auto-flag regressions >2%.
If you’d like more detail or want to loop in our Head of ML Ops or Graph Engineering before tomorrow’s call, just let me know. Looking forward to our discussion at 10 AM GMT!
Best,
J***e
Thanks J***e, that’s really helpful. Quick follow-up: with Neptune’s global cluster, how do you handle in-flight writes during schema migrations,are you using blue/green or backward-compatible rollout strategies? Also, is your nightly feature store a custom solution or built on something like Feast, and how do you ensure atomic refreshes without gaps?


Hi D***e,
Great questions,here’s how we tackle those two areas:
1. In-flight Writes & Schema Migrations in Neptune
- We treat most schema updates as backward-compatible (additive fields, new labels) and roll them out via versioned Liquigraph scripts in our CI/CD pipeline.
- During the migration window, our services are dual-write capable: they write to both old and new schema shapes, and we run smoke tests in a staging cluster to validate before cut-over.
- For truly breaking changes, we spin up a parallel (blue) Neptune cluster, let it catch up via replication, then switch traffic over in a single, coordinated step,ensuring zero-downtime and no lost writes.
2. Nightly Feature Store & Atomic Refreshes
- We’ve built a custom feature store on top of our Delta Lake layer (orchestrated by Airflow + Spark/Beam), rather than using a third-party tool like Feast.
- Every evening, features are computed into a temp Delta table; once all jobs succeed, we perform an atomic table swap (leveraging Delta’s transaction log) so consumers always see a full, gap-free view.
- We also validate row counts and key integrity before swapping and have alerting on any row-count drift to catch anomalies immediately.
If you’d like to dive deeper into any of this, I’m happy to loop in our Head of Data Infrastructure or ML Ops during tomorrow’s call. Looking forward to our discussion at 10 AM GMT!
Best,
J***e
HR Recruiter, A***x
Thanks, that helps a lot! Two quick follow-ups: during the dual-write migration window, how do you surface and react to lag or backpressure, and what’s your rollback playbook if a Liquigraph script misbehaves? And on the Delta swap, how do you coordinate downstream consumers to guarantee they never see a stale or half-baked table?


Hi D***e,
Happy to dive into those:
1. Dual-write lag/backpressure & rollback
- We surface consumer lag and backpressure via Grafana dashboards hooked to Kafka consumer‐group metrics and our own service-level backpressure gauges. If lag spikes, we throttle upstream producers, spin up extra consumer pods (auto-scaled by Kubernetes), and send immediate alerts to SRE and the PM/infrastructure teams.
- If a Liquigraph migration misbehaves, our CI/CD rollback playbook kicks in:
• Liquigraph tracks and applies both “up” and “down” scripts, so we can roll back the failed change with a single CLI command.
• We maintain a live backup snapshot of the pre-migration state, so if anything unexpected occurs, we restore via that snapshot, rerun smoke tests on staging, then reapply only the vetted subset of changes.
2. Delta swap & downstream consistency
- All consumers point to a stable view alias (rather than the raw table). In our Delta Lake, we build features into a temp table (e.g. `feature_updates_tmp`), validate row counts and key integrity, then use a single metadata transaction to atomically swap the alias from `feature_live` → `feature_updates_tmp`.
- Because the alias swap happens in one atomic commit, downstream readers never see a half-baked state,they instantly switch from the old snapshot to the new one. We also emit a post-swap event on our internal event bus so any long-lived processes can refresh their table pointers immediately.
Let me know if you’d like to walk through more edge cases or loop in our Head of Data Infrastructure. Looking forward to our chat tomorrow at 10 AM GMT!
Best,
J***e
HR Recruiter, A***x
Thanks for the extra detail! Given my work scaling Kafka consumers at U***y, I’m curious,what lag/SLO threshold do you use to trigger throttling versus spinning up pods? And on the alias swap, how do you guarantee long-running or newly started consumers don’t miss that post-swap event and end up pointing at a stale table?


Hi D***e,
Great questions,here’s how we handle both:
1. Lag/SLO thresholds & autoscaling
- We target consumer lag of under 30 seconds (roughly 1,000,1,500 messages) per partition. If lag creeps above that threshold for more than 20 seconds, our Kubernetes autoscaler spins up additional consumer pods. We only throttle upstream producers if lag exceeds twice the SLO (around 3,000 messages) for over a minute, giving us headroom to scale without impacting end-to-end latency. All of this is surfaced in Grafana so we can tune thresholds on the fly.
2. Alias swap & consumer consistency
- We publish every alias-swap event to our internal Kafka “metadata” topic and version it in a small DynamoDB-backed config service. Consumers subscribe to that topic and, on startup or on a swap event, pull the latest alias version atomically before proceeding. Long-running consumers also poll the config service every 5 minutes (or immediately on metadata-topic notification) so they never point at stale tables. This dual mechanism ensures zero window where a consumer could miss the swap.
Looking forward to diving into any further edge cases tomorrow at 10 AM GMT. Let me know if there’s anything else you’d like me to prep!
Best,
J***e