Data & BI is for teams that need a result they can measure. Most businesses are data-rich and insight-poor. We build warehouses, extract-transform-load (ETL) pipelines, and dashboards. Raw data becomes reliable, real-time intelligence your team can use.
A typical engagement includes Data Warehouses, ETL & Data Pipelines, and BI Dashboards. Delivery focuses on Data Audit and Semantic Layer. You also get Single source of truth, Self-service for non-technical teams, and Automated data quality checks.
Senior engineers who have shipped similar systems do the work. Common technical work includes Data Warehouses, Transformation, and Orchestration. Open the sections below for scope, or contact us with the outcome you need.
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Central storage for business data on Snowflake, Big Query, or Redshift. Models are built for fast analytical queries.
Automated pipelines extract, transform, and load data. Sources include CRMs, ERPs, databases, and APIs.
Interactive dashboards and reports in Looker, Metabase, Power BI, or Tableau. We design them for the people who actually use them.
Streaming pipelines with Kafka or Kinesis. Operational dashboards update in seconds, not hours.
Snowflake, Google Big Query, Amazon Redshift, Click House
dbt (data build tool) for modular, tested, version-controlled SQL transformations
Apache Airflow, Prefect, dbt Cloud for pipeline scheduling and monitoring
Looker, Metabase, Power BI, Tableau, Apache Superset, custom React dashboards
We map your sources, quality issues, and gaps first. Infrastructure comes after that picture is clear.
We define a shared metrics layer. Every team then calculates revenue, churn, and active users the same way.
Automated tests run on every pipeline. Schema checks, null checks, and anomaly alerts catch bad data before it hits a dashboard.
We train your team to build their own reports. The goal is self-sufficient analysts, not a queue on engineering.
Each engagement is scoped to your goals, but most projects include Data Warehouses, ETL & Data Pipelines, BI Dashboards, and Real-Time Analytics. We tailor deliverables to your product stage, team capacity, and business priorities.
Project timelines depend on complexity, integrations, and existing systems. Most engagements start with discovery and scoped planning, followed by iterative delivery milestones so you can review progress and ship value early.
Our stack is chosen based on your context, and commonly includes Data Warehouses, Transformation, Orchestration, and Visualization. We prioritize maintainability, performance, and long-term ownership by your internal team.
We follow a delivery process centered on Data Audit, Semantic Layer, and Data Quality and back it with Single source of truth, Self-service for non-technical teams, and Automated data quality checks. You receive clear documentation, production-ready handover, and ongoing support options after launch.