We architect end-to-end data engineering platforms — from ingestion pipelines and distributed data warehouses to real-time streaming analytics and executive BI dashboards — enabling every decision in your organisation to be grounded in empirical, real-time intelligence rather than intuition and spreadsheet approximation.
The organisations that will dominate the next decade are those that have mastered the discipline of converting data velocity into decision velocity. At RB Tech Services, we implement the Modern Data Stack — a cloud-native, modular architecture that replaces legacy ETL monoliths with composable, observable, and incrementally scalable data infrastructure built for the petabyte era.
From ingestion (Fivetran, Airbyte) through transformation (dbt) to storage (Snowflake, BigQuery) and visualisation (Power BI, Tableau, Metabase), we implement each layer with the rigor, governance, and documentation standards that separate a toy analytics setup from an enterprise-grade intelligence platform.
ELT pipelines via Fivetran, Airbyte, or custom connectors ingesting data from 100+ sources — databases, SaaS platforms, IoT sensors, APIs, flat files, and streaming event queues with schema evolution handling.
Snowflake, BigQuery, AWS Redshift, or Delta Lake on Databricks — columnar storage optimised for analytical query patterns, partitioning strategies, and cost-efficient cold storage tiering for historical data.
SQL-first transformation pipelines in dbt with modular data models, incremental materialisation, lineage graphs, automated testing, and CI/CD for your data transformation codebase.
Semantic layer with consistent business metric definitions, Power BI / Tableau / Metabase dashboards, embedded analytics APIs, and self-service data exploration for non-technical stakeholders.
Cloud-native data warehouse architecture on Snowflake, BigQuery, or Redshift — Kimball dimensional modelling, slowly changing dimensions (SCD Type 2), star schema design, materialised views, and query performance optimisation for sub-second analytical queries.
Apache Kafka and Apache Flink streaming pipelines processing millions of events per second, windowed aggregations, stateful stream processing, CDC (Change Data Capture) from operational databases, and real-time dashboards with sub-second latency.
Pixel-perfect Power BI and Tableau dashboards with DAX-optimised measures, row-level security (RLS), incremental refresh policies, mobile-responsive layouts, and scheduled report delivery — giving C-suite stakeholders always-current KPI visibility.
Great Expectations data quality framework implementation, data catalogue with column-level lineage (OpenMetadata, Datahub), PII classification and masking, data retention policies, and DPDPA-compliant data governance documentation for audit readiness.
Metabase or Apache Superset deployment for democratised self-service analytics, embedded analytics SDKs (Sigma, Cube.dev) integrated into product UIs, and natural language query interfaces powered by LLMs for SQL-free data exploration.
ML-augmented BI with forecasting models embedded directly into dashboards, anomaly detection alerts, scenario modelling engines, cohort analysis, RFM segmentation, and causal inference models for understanding what drives your most critical business metrics.
Stop operating on gut feeling. Let's build a data infrastructure that puts real-time, reliable intelligence at the fingertips of every decision-maker in your organisation.