Data Intelligence & Aggregation
From raw data to decisions.
End-to-end data pipelines and intelligence layers that turn siloed, inconsistent data into systems you can actually query and act on.
Most organizations have more data than they can use and less insight than they need. The gap between the two is not a data problem — it is a pipeline, transformation, and modeling problem. We close that gap: from raw sources to a data warehouse you trust, to dashboards and APIs that your teams actually use to make decisions.
What we build
Data intelligence infrastructure is a stack of interdependent components. We design and deliver the full stack — not individual pieces that leave integration gaps for your team to close.
ELT Pipelines (Airbyte, Fivetran)
We configure and deploy ELT pipelines that move data reliably from your source systems — databases, SaaS platforms, APIs, event streams — into your data warehouse. We use Airbyte for flexibility and cost efficiency, Fivetran where managed reliability and a broad connector catalog are the priority. We handle connector configuration, incremental sync strategies, error handling, and monitoring so data arrives on schedule and you know when it does not.
Data Warehouses (Snowflake, BigQuery, Redshift)
We design and implement the warehouse layer — schema design, table architecture, partitioning and clustering strategies, cost optimization, and access controls. We select the right warehouse for your workload: Snowflake for separation of storage and compute with enterprise governance, BigQuery for GCP-native workloads and serverless query pricing, Redshift for AWS-integrated environments and columnar performance. The warehouse is the foundation; we build it to hold weight.
Data Transformation (dbt)
We build dbt transformation layers that convert raw, inconsistent source data into clean, documented, tested data models that analysts and applications can trust. We design the project structure, write models with appropriate testing coverage, implement documentation, and configure CI/CD so transformations run and are validated on a schedule. dbt is where the raw data becomes the data your business actually uses — and where most teams underinvest in quality and testing.
Dashboards and BI
We build BI layers on top of your data warehouse — configuring tools like Metabase, Looker, or Superset, or building custom dashboard applications where the standard tools do not fit. We design dashboards that answer real business questions, not just display data. That means working with stakeholders to understand the decisions the dashboard needs to support, not just the metrics they ask to see.
Data Quality and Observability (Great Expectations, Monte Carlo)
Data pipelines fail silently. A table that looks populated but contains stale, incomplete, or anomalous data is more dangerous than a table that fails visibly — because decisions get made on bad data before anyone notices. We implement data quality frameworks using Great Expectations for expectation-based validation and Monte Carlo for automated anomaly detection and data lineage. You find out about problems before your stakeholders do.
Semantic Layers
Semantic layers — built with tools like dbt Semantic Layer, Cube, or LookML — abstract the complexity of your data model behind a consistent business vocabulary that analysts, BI tools, and AI applications can query without needing to understand the underlying schema. We design semantic layers that reflect how your business thinks about its data, not how it happens to be stored.
Our Data Stack
We select tools based on your requirements — cost profile, scale, existing infrastructure, and team capabilities. These are the technologies we have production experience with across the full data intelligence stack.
Ingestion & Integration
Airbyte for flexibility and cost efficiency with a broad connector catalog. Fivetran where managed reliability is the priority. Custom connectors for sources that no off-the-shelf tool covers — internal APIs, proprietary formats, and legacy systems with non-standard interfaces.
Transformation
dbt for SQL-based transformation with testing, documentation, and CI/CD. Custom Python pipelines for transformations that require procedural logic, external API calls, or data structures that do not fit the SQL model well.
Warehousing
Snowflake for enterprise governance and separation of storage and compute. BigQuery for GCP-native workloads and serverless pricing. Redshift for AWS-integrated environments. DuckDB for analytical workloads that run efficiently in-process without a managed warehouse.
Orchestration
Airflow for DAG-based scheduling in enterprise environments with broad operator ecosystem requirements. Dagster for asset-centric pipelines where data lineage and freshness tracking are the primary concern. Prefect for lightweight, Python-native orchestration without the operational overhead of running your own scheduler.
Quality & Observability
Great Expectations for expectation-based validation that catches bad data before it reaches downstream consumers. Monte Carlo for automated anomaly detection and data lineage across the warehouse. Custom alerting for SLA-critical pipelines where the standard tooling does not surface the right signals.
BI & Visualization
Metabase for self-serve analytics in organizations that want a lightweight, open-source BI layer. Looker for enterprises that need governed metrics, LookML-defined business logic, and tight control over how data is exposed. Custom dashboards for use cases that require interactivity, embedding, or data presentation that off-the-shelf tools cannot accommodate.
How We Approach It
Every data intelligence engagement follows the same four-phase approach. The details differ by client; the discipline does not.
1. Data Audit
We map your current state: every data source, its quality characteristics, the gaps between what exists and what is needed, and — critically — which business decisions are blocked because the data is not right. The audit produces a clear picture of the problem before any architecture is proposed.
2. Architecture Design
We design the target state before writing any code: warehouse selection and schema design, transformation layer structure, ingestion pipeline approach, and quality check placement. Architecture decisions are documented with rationale so you understand what was built and why, not just that it works.
3. Incremental Build
We build in layers. First pass: data is available and queryable. Second pass: quality is guaranteed through testing and validation. Third pass: the system is automated, observable, and requires no manual intervention to run. Each pass delivers value independently rather than waiting for the entire system to be complete before anything is usable.
4. Handoff & Documentation
You get a system you can run and extend without us. Every engagement delivers full operational runbooks, data dictionaries covering every table and field in the warehouse, and documented lineage from source to consumption layer. The goal is a data infrastructure your team owns, not one that requires the original builders to operate.
What we deliver data intelligence for
Data intelligence infrastructure underlies every category of business decision. These are the use cases where we see the most direct impact from getting the pipeline and modeling right.
Reliable, consistent metrics across the business — revenue, retention, activation, operational KPIs — that leadership and functional teams can trust and act on without debating the numbers.
Near-real-time visibility into operational processes — support queue health, fulfillment pipeline status, fraud signals — where decisions happen on minutes-to-hours timelines, not days.
Internal or external data products — APIs, feeds, and datasets — built on top of a curated, quality-validated data layer. Data products require the same rigor as software products: defined contracts, versioning, and quality guarantees.
Unified customer profiles assembled from CRM, product, support, billing, and behavioral data sources — the foundation for personalization, lifecycle marketing, and churn modeling.
Automated, auditable reporting pipelines for regulatory submissions — where data lineage, reproducibility, and access controls matter as much as the numbers themselves.
Why AR Data
Data engineering is one of the oldest things we do. The 20+ years of enterprise delivery behind AR Data is heavily concentrated in data infrastructure — building the systems that large organizations depend on for decisions, operations, and compliance. That background includes Oracle, where we built and delivered enterprise data systems; Iron Mountain, where we managed data pipelines for one of the world's largest records management businesses; and Scotiabank and Macquarie Bank, where data quality and auditability are existential concerns.
That depth means we recognize the failure modes before they happen. We have seen what siloed schema design does to a BI layer two years later. We know what happens to dbt projects that grow without testing standards. We understand what "trust the data" actually requires — and we build toward it from day one.
We use agentic workflows in our own build process, delivering faster without reducing the quality of what we ship. You get a data infrastructure built to enterprise standards, on a timeline that reflects how modern development actually works.
Ready to get more from your data?
30 minutes. We scope where your data lives, where it needs to go, and what a production data intelligence layer looks like for your environment. No pitch deck.
