Reliable data infrastructure
from raw to ready.
ETL pipelines, data warehouses, dbt transformation layers, and streaming infrastructure — engineered with the testing and monitoring that makes your analytics trustworthy rather than tolerated.
Live pipeline status
salesforce → warehouse
salesforce_opportunities
24,183 rows
4m 12s
stripe → revenue_mart
stripe_charges
891,204 rows
1m 38s
postgres → dim_customers
customers_clean
143,509 rows
2m 07s
Orchestrated with Airflow · dbt transformations passing · 3/3 pipelines healthy
Not the dashboard.
The infrastructure underneath it.
Data projects fail in a predictable sequence. Someone builds a dashboard that looks great in the demo. Leadership starts making decisions based on it. Then someone notices the numbers don't match the source system. An investigation reveals three different transformation scripts, two of them undocumented, one running on a laptop under someone's desk.
The dashboard gets abandoned. Trust in data collapses. Decisions go back to gut instinct. The problem was never the dashboard — it was the absence of reliable infrastructure underneath it.
We build the data infrastructure that makes analytics trustworthy: reliable ingestion, well-modeled warehouses, robust transformation logic, and the testing and monitoring that catch problems before they corrupt downstream decisions.
Eight layers,
one trustworthy platform.
ETL/ELT Pipelines
Pipelines that move data from your source systems — operational databases, SaaS platforms, APIs, event streams, flat files — into your data warehouse reliably at the schedule your analytics requirements demand. Error handling that catches problems at the source, alerting that surfaces failures before anyone discovers them through a wrong number, and idempotency guarantees that make pipelines safe to rerun.
Data Warehouse Design & Modeling
The foundation that everything else depends on. Warehouse architecture selection (Snowflake, BigQuery, Redshift, Databricks, ClickHouse), dimensional modeling (Kimball star schema, OBT, Data Vault), and the physical design choices that determine query performance and storage cost. We model data to match your business, not to fit a generic industry template.
dbt Transformation Layer
SQL-based transformation logic built in dbt with the software engineering discipline — version control, testing, documentation, modular design — that production data systems require. Staging, intermediate, and mart models organized in a dependency graph. dbt tests catch data quality issues in the pipeline rather than in a boardroom.
Real-Time Streaming Pipelines
When batch processing latency is too high — fraud detection, real-time inventory, live customer behavior, manufacturing quality monitoring — we build the streaming infrastructure that processes events as they happen. Kafka, Kinesis, Pub/Sub, or Flink based on scale and operational capabilities.
Data Lakehouse Architecture
Combining the raw storage flexibility of a data lake with the query performance and ACID transaction support of a warehouse. Delta Lake, Apache Iceberg, and Apache Hudi table formats that enable reliable analytics directly on object storage — without the schema rigidity of warehouses or the query limits of raw lakes.
Data Quality Frameworks
Data quality isn't a feature — it's the prerequisite for everything else. Automated monitoring: row count validation, freshness checks, distribution anomaly detection, referential integrity, and schema drift detection. Built on Great Expectations, dbt tests, and your warehouse's native capabilities.
Pipeline Orchestration
Coordinating complex multi-step pipeline workflows with dependencies, retries, backfills, parallel execution, and the operational observability that lets you understand what's running, what's failed, and why. Airflow, Dagster, Prefect, or dbt Cloud based on workflow complexity.
Data Governance & Cataloging
As data infrastructure matures, the challenge shifts from building pipelines to managing them. Catalogs that document every table, column, and lineage relationship. Access control policies that enforce data classification. Retention policies that manage storage costs and compliance.
Every analytics workload
needs a foundation.
Whether you're building analytics, training ML models, or satisfying auditors — the requirement is the same: data infrastructure you can depend on.
Analytics & Business Intelligence
The foundational infrastructure for any analytics capability. Clean, modeled, well-documented datasets in a data warehouse that your BI tools, analytics teams, and self-serve users can query reliably. Pipelines that keep the warehouse current without manual intervention. Data models that encode business logic consistently so every analyst gets the same answer to the same question.
Machine Learning & AI Infrastructure
Training data pipelines that prepare features from raw operational data. Feature stores that compute and serve ML features consistently across training and inference. Data versioning that allows model experiments to be reproduced. The data infrastructure that makes ML projects succeed rather than stall on data quality problems.
Operational Analytics & Real-Time Monitoring
Streaming pipelines that feed operational dashboards and alerting systems with near-real-time data. Event processing that triggers automated workflows based on data conditions. The infrastructure that closes the loop between what's happening in operations and what actions get taken.
Data Products & External Data Sharing
Building data assets that are shared with customers, partners, or other internal teams as a product — not just an extract. Governed, documented, versioned data products with the access controls, SLAs, and quality guarantees that make them dependable.
Compliance & Audit Data Infrastructure
Data pipelines designed for regulatory compliance: immutable audit logs, data lineage documentation, access control enforcement, retention management, and the reporting infrastructure that satisfies auditors and regulators. SOX, HIPAA, GDPR, PCI DSS — compliance drives architecture when we design for it from the start.
From data audit
to production pipeline.
Understand the data landscape.
Source system inventory, data quality assessment, volume and velocity analysis, and use case prioritization. Before designing the architecture, we need to understand what data exists and what state it's in.
Design the architecture.
Warehouse selection, dimensional model design, pipeline architecture, orchestration approach, and the data quality framework. Documented with rationale — not just what we chose, but why, and what tradeoffs it involves.
Build incrementally.
Pipelines built one domain at a time, with each domain tested and validated before the next begins. Incremental delivery means errors are caught in the right context, early enough to fix.
Instrument with testing.
dbt tests and custom validation logic written in parallel with transformation code — not added afterward. Every model has tests. Data quality issues surface in the pipeline, not in a downstream report.
Deploy and document.
Production deployment with monitoring, alerting, and runbooks. Pipeline performance dashboards. Freshness SLA monitoring. Data catalog entries for every model — business definitions, lineage, ownership, refresh cadence.
The stack we reach for.
Warehouse selection, streaming framework, and orchestration tool are chosen based on your scale, query patterns, team capabilities, and cost model — not familiarity bias.
Infrastructure that
earns trust over time.
We treat data infrastructure like software.
The production data pipeline that runs at 2am is software. It needs version control, code review, automated testing, documentation, and monitoring — the same engineering discipline as any production application. The difference shows up in reliability over time.
We build trust in the data.
Success isn't whether the pipelines run — it's whether your teams trust the numbers and make decisions based on them. That requires quality testing that catches errors before they reach analysts, lineage that lets people trace numbers back to source, and practices that keep definitions consistent.
We design for the team that will maintain it.
Data infrastructure outlasts the team that built it. We design with the eventual maintainers in mind — documentation, observability that makes debugging tractable, and architecture complexity that matches the operational capabilities of the team that will run the system.
We start with business requirements, not technology choices.
The warehouse, orchestration tool, and streaming framework are implementation decisions that follow from understanding what decisions the data needs to support and what the operational team is capable of maintaining. We don't recommend infrastructure because it's interesting — we recommend it because it fits.
Straight answers.
Source system data quality problems are the most common cause of analytics trust failures. We address them at every layer: source system audits during the assessment phase, validation in the ingestion layer that quarantines bad data rather than loading it, dbt tests that validate business rules, and monitoring that alerts when data quality metrics degrade. We also surface source system quality issues to the teams responsible for them — fixing the source is often more effective than cleaning the downstream symptom.
It depends on your team's existing cloud investments, your query patterns, your data volume, and your cost model. Snowflake's separation of storage and compute is valuable for variable workloads; BigQuery's serverless model is operationally simpler; Redshift integrates well with existing AWS infrastructure. We'll give you an honest recommendation based on your specific context — not based on partnership relationships or familiarity bias.
Not necessarily. Kafka is powerful but operationally complex. For many real-time use cases, managed services like AWS Kinesis, Google Pub/Sub, or Azure Event Hubs provide sufficient capability with significantly lower operational overhead. We recommend Kafka when the use case requires its specific capabilities — high throughput, log compaction, complex consumer group patterns — and simpler managed services otherwise.
Pipeline reliability architecture is designed upfront: retry logic with exponential backoff, dead letter queues for unprocessable records, alerting for SLA breaches, and runbooks for the failure modes we've anticipated. For each pipeline, we define the acceptable failure behavior — fail fast and alert, retry with backoff, skip and log, or quarantine for review — based on the downstream impact of different failure types.
It depends on complexity and change rate. A well-designed warehouse with 50–100 dbt models and a handful of ingestion pipelines can be maintained by a single data engineer with reasonable effort. A platform supporting real-time streaming, 500+ models, and 20 source systems warrants a dedicated team. We design for the operational team you have, and we document in a way that reduces the bus factor.
Build data infrastructure you can depend on.
Tell us about your data landscape and what you're trying to build on top of it.