Data Engineering & Analytics

Data platforms that are trusted, fast, and AI-ready.

We build modern data platforms on Snowflake, BigQuery, Databricks, and Fabric — with streaming ingestion, dbt-governed modeling, a semantic layer your stakeholders actually trust, and feature stores that feed every downstream ML and LLM workload.

Pipelines live

148

< 2s lineage refresh

Freshness SLA

99.94%

Rolling 30-day p99

12TB+Daily event throughput on largest cluster

-54%Average warehouse spend after rearchitecture

99.94%Freshness SLA across production pipelines

6 wksFrom kickoff to first governed dashboard

From raw events to AI-ready products

We build the end-to-end data platform your business, analytics, and AI teams share — not a stack of disconnected tools.

bolt

Streaming ingestion

CDC, Kafka, Kinesis, and event-driven ingestion with exactly-once semantics, schema evolution, and back-pressure handling.

check_circleDebezium, Fivetran, Airbyte
check_circleKafka, Kinesis, Pub/Sub
check_circleSchema registry + data contracts

architecture

Lakehouse & warehouse

Iceberg and Delta lakehouses, Snowflake and BigQuery warehouses — designed to be cheap, fast, and observable.

check_circleSnowflake, BigQuery, Databricks, Fabric
check_circleIceberg + Delta table formats
check_circleFinOps dashboards + autoscaling

schema

Modeling & semantic layer

dbt, SQLMesh, and Cube.dev to turn raw data into governed metrics and a single semantic layer every tool can speak.

check_circledbt + SQLMesh transformations
check_circleCube / MetricFlow semantic layer
check_circleCertified metric catalog

monitoring

Observability & quality

Data freshness SLAs, anomaly detection, and column-level lineage — so you trust the number before leadership asks.

check_circleMonte Carlo, Anomalo, Elementary
check_circleOpenLineage + column-level lineage
check_circlePagerDuty-wired freshness SLAs

lock

Governance & access

Role-based, row-level, and column-masked access with purpose-based policies — audit-ready from day one.

check_circleUnity Catalog, Polaris, Horizon
check_circleImmuta, Privacera policy-as-code
check_circleSOC 2, HIPAA, FedRAMP aligned

smart_toy

Feature & vector stores

Online + offline feature stores, embedding pipelines, and vector indexes — the plumbing every ML and LLM workload needs.

check_circleFeast, Tecton, Databricks FS
check_circlePinecone, Weaviate, pgvector
check_circleRetrieval-augmented generation stacks

A repeatable path from chaos to clarity

We bring a proven four-step method that de-risks data platform builds — so business value ships in weeks, not quarters.

Assess

A 2-week sprint to map data sources, critical metrics, pain points, and platform debt — delivered as a scored maturity report.

check_circleStakeholder interviews
check_circleStack + cost audit
check_circlePrioritized 90-day plan

Architect

Reference architecture tailored to your scale, privacy posture, and cloud — with clear build-vs-buy decisions on every layer.

check_circleTarget-state diagram
check_circleTool selection memo
check_circleCost + FinOps model

Build

Iterative delivery: first governed mart in 6 weeks. Pipelines, models, and dashboards ship in vertical slices with tests.

check_circleCI/CD + dbt tests
check_circleObservability baked in
check_circleBiweekly demos

Operate

We hand over the runbook — or stay as an embedded platform team. Either way, freshness SLAs and on-call are well-defined.

check_circleRunbooks + playbooks
check_circlePlatform team enablement
check_circleManaged SRE (optional)

The data stack we deploy

Right tool for the job, wired for your cloud

We deploy across AWS, Azure, and GCP — with battle-tested reference architectures and a clear bias for composable, interoperable tools.

storage

Warehouse / lakehouse

SnowflakeBigQueryDatabricksMicrosoft FabricRedshift

input

Ingestion & CDC

FivetranAirbyteDebeziumKafka ConnectStitch

transform

Transformation

dbt CloudSQLMeshDagsterAirflowPrefect

analytics

BI & activation

LookerHexModeTableauHightouchCensus

Data platforms in production

Shipped by teams who answer to the numbers

We build data platforms for operators who need answers today — not research projects.

shopping_cart

E-commerce

Unified customer, catalog, and order data across 14 countries on Snowflake — powering pricing, inventory, and personalization.

trending_up-54% warehouse spend

account_balance

Financial services

Regulated lakehouse on Databricks with Unity Catalog, column-level masking, and line-of-business data products.

trending_upSOC 2 + SOX ready

local_hospital

Healthcare

HIPAA-aligned data mesh: clinical, claims, and operational data as governed products with PHI masking and BAA-covered access.

trending_up17 data products

factory

Manufacturing & IoT

High-throughput sensor ingestion on Kafka + Iceberg — predictive maintenance models fed by a shared feature store.

trending_up-31% unplanned downtime

gavel

Public sector

FedRAMP-aligned analytics platform on GovCloud with role-based access, full audit trail, and 508-compliant dashboards.

trending_upFedRAMP Moderate

subscriptions

Media & subscription

Semantic layer + metric certification so product, finance, and marketing finally agree on MRR, ARPU, and churn.

trending_up1 source of truth

Common questions

Data platform questions, answered honestly

It depends on your workload shape, cloud commitments, and existing skills. We evaluate TCO, query profile, and team fluency before recommending. We ship on all three, so our advice is not biased by a reseller agreement.

Almost always yes. A FinOps audit typically finds 30–60% savings through query refactoring, warehouse right-sizing, clustering strategy, table format choice, and killing dead pipelines. We have a standard 2-week diagnostic.

dbt tests on every model, column-level lineage, freshness SLAs, anomaly detection, and certified metrics in the semantic layer. If a pipeline breaks, the owner is paged — and the dashboard banner shows it is stale.

Three prerequisites: governed entities, a feature store for structured signals, and a clean, chunked, embedded content store for retrieval. We design warehouses with these downstream consumers in mind from day one.

We do both — but enablement is the default. Pair programming, internal docs, code reviews, and runbook handover are baked into every engagement. Goal: your team owns it on day 91.

Make your data a product, not a backlog.

Two-week data platform audit. We will benchmark your current stack on cost, freshness, and trust — and hand you a 90-day roadmap you can ship against immediately.