Data Analytics Systems I Build for Startups

A durable analytics platform is more than a dashboard. It is instrumentation, event pipelines, a warehouse you can trust, near-real-time streams for decisioning, and a clear path from raw signals to business outcomes. On this page I outline several data analytics projects I delivered for consumer products with tens of millions of users and terabytes of data, and the approaches I use to make them scalable, observable, and cost-efficient.

Event Instrumentation & Collection

Video telemetry with Bitmovin Analytics

For media and live content products, I implemented end-to-end video analytics based on the Bitmovin SDK:

In-app event capture: custom playback, buffering, quality and engagement events emitted by the Bitmovin mobile SDKs.
Authoritative storage: events persisted in the Bitmovin Analytics platform.
Warehouse ingestion: bulk export from Bitmovin via the Data Export API into Snowflake, with schema-controlled tables that support session reassembly and cohort analysis.

This pipeline enables accurate QoE (quality of experience) monitoring, content performance reporting, and experiment readouts—without embedding heavy logic in client apps.

Push-notification marketing telemetry at massive scale

To measure campaign effectiveness across tens of millions of subscribed users:

Ingress: API events collected by internal services.
Streaming: events published to Pub/Sub, then aggregated JSON written to GCS.
Warehouse load: ingestion into Snowflake through Snowpipe.

The design supports high-throughput writes, deterministic aggregation windows, and replayability for backfills—crucial for attribution and channel ROI analysis.

Storage & Processing: Snowflake-Centric Warehouse

I operate Snowflake as the system of record for analytics at TB-scale, tuned for both concurrency and cost.

Workload-aware warehouses: multiple virtual warehouses mapped to ELT, BI, data science, and ad hoc profiles.
Cost/performance “efficient frontier”: benchmarked queries and warehouse sizes to choose the optimal point on the compute-spend vs. latency curve for each workload.
Snowpipe for continuous loads with file-level traceability and retry.
Airflow for orchestration (SLA-aware DAGs, backfill windows, and dependency management).
CDC into Snowflake: near-real-time replication from CockroachDB (and Postgres) via Change Data Capture for transactional facts that must stay fresh.

Streaming & Backfills to Elasticsearch

For in-app user search and near-real-time analytics dashboards:

Hot path: API → Pub/Sub → Dataflow → Elasticsearch (seconds-to-minutes freshness typical).
Cold/backfill path: Snowflake → GCS → Dataflow → Elasticsearch.
Long-running backfill pipeline: built to incorporate multiple third-party sources (including external AI model providers and internal APIs). A custom autoscaler on Kubernetes regulates the number of processing workers based on error-free throughput, average processing time from logs, and model response latencies—keeping SLOs stable while controlling cost.

Data Lake on AWS

Some clients required a data-lake architecture on AWS for scale and flexibility. I delivered a custom data loading and transformation engine capable of 10B+ rows per day:

Ingest: Kinesis / Kinesis Firehose → S3 (partitioned, compressed objects with consistent metadata).
Staging & ELT: Redshift Spectrum for external table staging and SQL-based transformations.
Curated lake: transformed data materialized back to S3 as optimized, columnar datasets.
Interactive analytics: Athena for ad-hoc queries over the curated lake.

This pattern cleanly separates raw, staged, and curated layers, keeps storage inexpensive, and gives analysts fast, serverless access when they need it.

Transformation, Modeling & Orchestration

dbt for data modeling, tests, and lineage—codifying business logic in version-controlled, reviewable SQL.
Airflow for batch and micro-batch jobs, with parallelized sub-tasks and containerized workers to elastically process larger volumes.
Quality gates (schema validation and row-level checks) enforced before data progresses into shared marts.

Visualization & Decision Support

Grafana on Kubernetes

For internal, finance, and executive teams I deploy custom Grafana builds on Kubernetes:

Data sources: Snowflake (via plugins) and internal HTTP APIs.
Dashboards: curated views for product, operations, and finance with templating and SSO/RBAC.
Ops readiness: dashboards for pipeline health, unit costs per job, and warehouse utilization.

Kibana for Near-Real-Time Dashboards

Where product teams need second-to-minute freshness, I pair the Elasticsearch stream with Kibana:

Use cases: live campaign monitors, growth funnels, and content engagement.
Benefits: quick exploratory slices over hot indices, with guardrails on retention and index lifecycle.

Reliability, Observability & Governance

A strong analytics foundation is as much about safety and trust as it is about speed.

Data contracts & schemas: typed events and table contracts to prevent breaking changes.
Idempotency & deduplication: deterministic identifiers and windowed compaction for replay safety.
DLQs & reprocessing: dead-letter queues with targeted replays; backfills managed through parameterized Airflow/Dataflow jobs.
Lineage & testing: dbt tests (unique, not-null, relationships) and lineage graphs for impact analysis.
Observability: pipeline metrics (throughput, lag, error rate), Snowflake warehouse telemetry, and autoscaler signals (processing times, model latencies).
Security: role-based data access across Grafana/Kibana/Snowflake, and separation between PII and analytics layers.

Selected Outcomes

Unified video analytics and engagement telemetry feeding Snowflake—enabling consistent product KPIs and experiment analysis.
Marketing pipeline measuring push notifications across tens of millions of subscribers, with attribution and channel performance readouts.
Near-real-time Elasticsearch dashboards for product and operations, backed by replayable streams and scalable backfills.
AWS data lake capable of 10B+ daily rows with cost-effective storage and fast ad-hoc analytics in Athena.
Executive and finance-grade Grafana reporting with reliable refresh SLAs and clear run-cost visibility.

How I Engage

Discovery & Assessment – Current state, data sources, SLAs, compliance, and stakeholder KPIs.
Architecture & Plan – Reference architecture, cost/performance modeling, and a roadmap with milestones.
Implementation – Instrumentation, pipelines, modeling, tests, and dashboards; parallel tracks for hot and cold paths.
Hardening & Handover – Backfill strategy, runbooks, observability, and knowledge transfer to your team.

Technologies at a Glance

Warehouse & Lake: Snowflake, Redshift Spectrum, Athena, S3, GCS
Streaming Pipelines & Orchestration: Snowpipe, CockroachDB (CDC), Dataflow (Apache Beam on GCP), Kinesis, Pub/Sub, Airflow
Modeling & Governance: dbt, data contracts, schema validation
Visualization: Grafana (usually custom docker builds deployed on Kubernetes), Kibana, Looker

If your team needs an analytics stack that scales with your product—and turns raw signals into decisions—I can help you get from architecture to production with clear SLAs, predictable costs, and measurable outcomes.