Data Engineering & Analytics

Data platforms for analytics and AI.

We build data platforms on Snowflake, BigQuery, Databricks, and Fabric with streaming ingestion, dbt-governed modeling, shared metric definitions, and feature stores for downstream ML and LLM workloads.

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 connect ingestion, storage, modeling, and governance in a data platform shared by your business, analytics, and AI teams.

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, plus Snowflake and BigQuery warehouses, with cost controls, performance tuning, and monitoring.

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

schema

Modeling & semantic layer

Use dbt, SQLMesh, and Cube.dev to define governed metrics and a shared semantic layer for reporting tools.

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 help teams trace reported numbers and identify quality issues.

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

lock

Governance & access

Role-based and row-level access, column masking, and purpose-based policies with evidence for audit review.

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

smart_toy

Feature & vector stores

Online and offline feature stores, embedding pipelines, and vector indexes for ML models and retrieval applications.

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

A delivery plan from assessment to operations

We assess your data and requirements, design the platform, deliver in stages, and establish operating responsibilities.

Assess

A 2-week sprint maps data sources, critical metrics, pain points, and platform debt in a scored maturity report.

check_circleStakeholder interviews
check_circleStack + cost audit
check_circlePrioritized 90-day plan

Architect

A reference architecture tailored to your scale, privacy requirements, and cloud, with build-versus-buy decisions for each 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_circleMonitoring included
check_circleBiweekly demos

Operate

We hand over the runbook or continue as an embedded platform team, with defined freshness SLAs and on-call responsibilities.

check_circleRunbooks + playbooks
check_circlePlatform team enablement
check_circleManaged SRE (optional)

The data stack we deploy

Tools selected for your cloud and workloads

We deploy on AWS, Azure, and GCP using reference architectures and tools that integrate with your existing systems.

storage

Warehouse / lakehouse

SnowflakeBigQueryDatabricksMicrosoft FabricRedshift

input

Ingestion & CDC

FivetranAirbyteDebeziumKafka ConnectStitch

transform

Transformation

dbt CloudSQLMeshDagsterAirflowPrefect

analytics

BI & activation

LookerHexModeTableauHightouchCensus

Data platforms in production

Data platforms across industries

Examples of data platforms supporting pricing, operations, reporting, and analytics.

shopping_cart

E-commerce

Unified customer, catalog, and order data across 14 countries on Snowflake to support 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, with a shared feature store for predictive maintenance models.

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

A semantic layer and metric certification give product, finance, and marketing shared definitions of MRR, ARPU, and churn.

trending_up1 source of truth

Common questions

Questions about data platforms

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.

Our standard 2-week FinOps diagnostic reviews queries, warehouse sizing, clustering, table formats, and unused pipelines. These audits typically identify 30 to 60% savings.

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 receives an alert and a dashboard banner marks the data as stale.

Three prerequisites: governed entities, a feature store for structured signals, and a clean, chunked, embedded content store for retrieval. We include these requirements in warehouse design.

Team training is part of each engagement through pair programming, internal documentation, code reviews, and runbook handover. Our goal is for your team to own the platform by day 91.

Plan your next data platform.

In a two-week audit, we assess your data platform for cost, freshness, and quality, then provide a prioritized 90-day roadmap.

Request an auditarrow_forward