Petabyte-scale ingestion, governed bronze–silver–gold layers, and sub-second BI—without locking you into a single vendor’s SQL dialect or proprietary notebook runtime.

Turn fragmented operational data into a governed analytics backbone your CFO and ML teams can trust

We design lakehouse topology, streaming and batch pipelines, identity-aware access, and cost controls so growth in data volume does not mean growth in surprise bills or audit findings. The outcome is a single place where RevOps, product, and data science agree on definitions, lineage, and freshness—backed by infrastructure you can operate or hand off cleanly. Whether you are consolidating siloed Postgres replicas, mainframe extracts, SaaS exports, or high-cardinality event streams, the architecture prioritizes idempotent writes, schema evolution, and replayability so incidents become recoverable stories instead of permanent gaps.

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Enterprise Data Lake & Real-Time Analytics Platform

01 // THE MANDATE

Petabyte-scale ingestion, governed bronze–silver–gold layers, and sub-second BI—without locking you into a single vendor’s SQL dialect or proprietary notebook runtime.

We design lakehouse topology, streaming and batch pipelines, identity-aware access, and cost controls so growth in data volume does not mean growth in surprise bills or audit findings. The outcome is a single place where RevOps, product, and data science agree on definitions, lineage, and freshness—backed by infrastructure you can operate or hand off cleanly.

Whether you are consolidating siloed Postgres replicas, mainframe extracts, SaaS exports, or high-cardinality event streams, the architecture prioritizes idempotent writes, schema evolution, and replayability so incidents become recoverable stories instead of permanent gaps.

02 // ENGINEERING

Development process

Structured phases—from discovery to launch—with clear ownership and handoff points.

Phase A — Discovery & data cartography (weeks 1–3)

We inventory sources, volumes, latency requirements, and compliance context (GDPR, HIPAA, SOC2, industry-specific rules). Workshops produce a logical domain map: which entities are global, which are tenant-scoped, and where conflicting definitions exist today. We document SLAs for freshness, acceptable downtime, and reconciliation tolerances versus finance systems.

Phase B — Foundation & landing zone (weeks 3–7)

We stand up the cloud landing zone or Kubernetes data platform: VPC isolation, KMS-backed encryption, service accounts with least privilege, network egress controls, and secrets rotation. Raw landing buckets or topics receive data with checksums, ingestion timestamps, and source metadata. Schema registries and compatibility policies prevent silent breakage.

Phase C — Processing & quality (weeks 6–12)

Bronze ingestion jobs are wired with retries and backpressure. Silver transforms apply cleansing, deduplication keys, and conformed dimensions. Gold marts expose subject areas (revenue, product usage, support) with documented grain and slowly changing dimensions. Quality tests block merges on failure; owners get Slack or email with failing row samples.

Phase D — Consumption & hardening (weeks 10–16)

BI tools connect through the semantic layer or curated views. Key dashboards are load-tested. We run parallel validation against legacy reports until variances are within agreed thresholds. Penetration testing and data access reviews precede production sign-off.

Phase E — Enablement & handover (weeks 14–18)

Runbooks cover on-call, scaling triggers, and how to add a new source. Training sessions for analysts and engineers include how to propose schema changes, how to trace lineage, and how to estimate cost impact of new workloads.

03 // CAPABILITIES

Core Capability Matrix

The building blocks of your solution

Lakehouse core

Open table formats (Iceberg/Delta) with ACID commits, partition evolution, and time travel for reproducible reports and ML training snapshots.

Ingestion plane

Kafka/Pulsar-compatible streaming, scheduled batch loads, and CDC from OLTP with dead-letter queues, ordering keys, and exactly-once semantics where the source allows.

Transformation

dbt or Spark jobs in isolated environments; environments per domain team with shared global dimensions and naming conventions enforced in CI.

Query federation

Trino/Presto or BigQuery-style interfaces across zones; optional semantic layer so BI tools hit stable business entities instead of raw tables.

Data quality

Great Expectations-style contracts, anomaly detection on volume and latency SLAs, and blocking gates before silver/gold promotion.

Security & governance

Row- and column-level policies tied to your IdP; column masking for PII; full audit trail of who queried what and which job wrote which snapshot.

Cost & ops

Storage tiering, compaction strategies, autoscaling worker pools, and chargeback dashboards by domain so owners see the bill before finance does.

ML feature store hooks

offline/online consistency, point-in-time correct joins, and feature lineage back to raw sources for regulatory review.

Disaster recovery

Cross-region replication for critical datasets, RPO/RTO targets, and runbooks for corruption or misconfiguration events.

Observability

Pipeline metrics in your existing stack (Prometheus/Grafana/Datadog), log correlation IDs from ingest through transform, and pager routing by owning team.

04 // DELIVERY LIFECYCLE

The strategic roadmap

Milestones and checkpoints—each phase has a clear outcome before the next begins.

Milestone 01Delivery

Weeks 1–3: Stakeholder interviews, source inventory, risk register, and high-level architecture sign-off. Deliverables: domain map v1, non-functional requirements, and a phased cost model.

Milestone 02Delivery

Weeks 4–8: Landing zone live; first bronze pipelines for two priority sources; initial monitoring dashboards; security baseline review with your InfoSec team.

Milestone 03Delivery

Weeks 7–12: Silver layer for core entities; first gold mart for executive KPIs; parallel reconciliation with existing reporting; quality contracts in CI.

Milestone 04Delivery

Weeks 11–16: Remaining sources by priority; semantic layer coverage for primary BI use cases; load and failover drills; documentation and training.

Milestone 05Delivery

Weeks 15–20: Hardening sprint—cost optimization, backlog of tech debt, formal handover, and optional managed operations transition.

Milestone 06Delivery

Ongoing: Quarterly architecture reviews, capacity planning, and roadmap for new domains (e.g. marketing attribution, IoT, finance subledger).

05 // PRODUCT SCOPING

Choosing your path

Two engagement models—start lean and iterate, or commit to a full platform build from day one.

MVP

Speed & essentialism

Phase 1
Scope limited to one business domain (e.g. sales pipeline + revenue recognition inputs) and up to three primary data sources. Includes bronze + silver for those entities, one gold mart, basic RBAC, nightly or near-real-time freshness (15–60 min) depending on source, and dashboards for a single leadership team. Excludes full enterprise semantic layer, ML feature store, cross-region DR, and broad CDC from every legacy system. Ideal when you need a credible internal reference implementation before funding a wider program. Out of scope for MVP: custom mobile apps, full self-service data catalog with automated PII classification across all columns, and 24/7 production support beyond business hours unless added as a separate line item.
Recommended

Full product

Enterprise maturity

All-in
Full program spans all agreed domains, all prioritized sources, streaming where latency demands it, and gold marts for finance, product, marketing, and operations. Includes semantic layer, feature store integration, cross-region replication for Tier-1 datasets, comprehensive data catalog with lineage UI, fine-grained IAM, and SOC2-aligned evidence packs. Adds chaos testing for pipelines, cost guardrails with automated throttling, and optional embedded analytics in your product for end-customers. Typically paired with a long-term SRE or platform engineering engagement: on-call rotations, quarterly disaster recovery exercises, and continuous improvement backlog owned jointly with your internal data platform team.

06 // PARTNERSHIP

Why work together

A single accountable partner across strategy, build, and go-live—not a revolving door of vendors.

John Hambardzumian
Direct collaboration

End-to-end ownership: discovery, architecture, implementation, and launch—with clear communication and production-grade engineering.

  • Discovery & alignment
  • Systems that scale
  • Implementation depth
  • Clear comms

07 // CLARITY

Frequently asked

We tie every increment to a business metric: time to monthly close, forecast accuracy, ticket deflection, or model refresh cadence. The first milestones deliver something leadership can open in a dashboard—not a blank S3 bucket. Scope is sequenced so value compounds: conformed dimensions land before dependent marts, and we never promise twenty sources in parallel without proven patterns for the first two.

08 // MORE SOLUTIONS

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