Transform raw data into a strategic asset — with scalable pipelines, intelligent analytics, and machine learning solutions that power smarter decisions.
We build the foundations that turn scattered data into a competitive advantage — from ingestion pipelines to predictive models and real-time dashboards your leadership team will actually use.
We design your data lakehouse, warehouse, or mesh architecture to serve today's needs and tomorrow's scale.
Reliable, monitored data pipelines that move, transform, and load data from any source to any destination.
Predictive models, recommendation engines, and NLP solutions that automate insight and decision-making.
Interactive Tableau, Power BI, or Looker dashboards that make complex data instantly understandable.
Kafka, Spark, and cloud-native solutions for real-time analytics at any scale.
AWS · Azure · Google Cloud
Snowflake · BigQuery · Redshift
Airflow · dbt · Prefect
Kafka · Kinesis · Pub/Sub
Python · Spark · TensorFlow
Tableau · Power BI · Looker
Data engineering is the practice of designing, building, and maintaining infrastructure that collects, stores, transforms, and delivers data reliably for analytics and AI. Without a solid data foundation, dashboards are inaccurate and AI models are unreliable. According to Gartner, poor data quality costs organisations an average of $12.9 million per year. RevOps Agentic builds data pipelines that deliver clean, governed, timely data to business and data science teams.
RevOps Agentic designs and builds solutions on Snowflake, Databricks, Amazon Redshift, and Google BigQuery. For transformation and data modelling we use dbt (data build tool). Orchestration is handled with Apache Airflow or Prefect. Visualisation and BI is delivered through Power BI, Tableau, and Looker. We select the right stack for your data volume, team skill set, and cost profile.
dbt (data build tool) is an open-source transformation framework that allows data teams to write SQL-based transformations as version-controlled, testable code. It replaces error-prone spreadsheet and stored-procedure workflows with modular, documented data models. Teams using dbt report 50–70% faster time to insight and significantly fewer data quality incidents. RevOps Agentic is a certified dbt implementation partner.
A foundational Snowflake or Redshift data warehouse with 3–5 integrated source systems typically takes 8–14 weeks to build. This includes data discovery, source system profiling, schema design, pipeline development, testing, and BI layer delivery. More complex projects with 10+ sources or real-time streaming requirements run 16–24 weeks. We deliver value incrementally — clients typically have their first dashboard within 4 weeks of kick-off.
Data quality is enforced at every layer of the pipeline. In the ingestion layer we validate schema, completeness, and referential integrity. In the transformation layer dbt tests check for nulls, duplicates, and business rule violations on every model run. We implement data cataloguing using tools like Alation or dbt's built-in documentation. A data SLA dashboard gives teams real-time visibility into freshness and quality scores across all datasets.
Let's design a data architecture that scales with your ambitions.
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