Data Engineering & Analytics
Reliable data pipelines and warehouses — from messy inputs to trusted reports.
Build robust, scalable, and audit-ready data pipelines and data warehouses. We design data systems for the messy realities of real-world business — handling schema drift, multi-supplier ingestion, and late-arriving data without silent failures.
What breaks without this
Data trapped in operational silos, slow or unreliable report generation, schema changes breaking pipelines, and lack of trust in data quality.
Business reports take 24–48 hours to generate and are already outdated when they arrive
A supplier changes their CSV format and the entire pipeline silently produces wrong numbers
Data is spread across 5 different tools with no single source of truth
Data analysts spend 60% of their time cleaning data instead of generating insights
How we deliver this
Data landscape audit
We map every data source, its format, update frequency, and ownership. We identify the single most impactful pipeline to build first — usually the one blocking the most business decisions.
Data contract & warehouse design
Avro or JSON Schema contracts are defined at ingestion. Dimensional models (fact/dimension tables) are designed in dbt with full column-level documentation.
Pipeline build & orchestration
DAGs are built in Apache Airflow or dbt Cloud. Data quality tests run on every pipeline execution and alert when records fail validation.
BI layer & analyst enablement
A semantic layer in dbt Metrics or LookML connects the warehouse to your BI tool (Tableau, Power BI, Looker). Analysts get pre-built report templates they can extend themselves.
What you gain
Centralized, query-optimized data warehouse (single source of truth) using Snowflake or BigQuery.
Robust ETL/ELT pipelines with explicit data contracts and automated schema validation.
Near real-time streaming data ingestion using Apache Kafka or AWS Kinesis.
Automated data quality monitoring and anomaly alerting on pipeline runs.
Self-service BI integration for business analyst enablement and reporting.
Concrete deliverables
Every engagement ends with tangible artefacts you own and can hand to any team.
Data architecture diagrams and dimensional models.
dbt project repository and ETL/ELT DAG configurations.
Centralized data warehouse deployment (Snowflake/BigQuery).
Data quality exception reporting dashboards.
BI dashboard templates (Tableau / Power BI / Looker).
Technologies we use
We select tools based on your requirements — not on what we happen to know best.
Success stories
See how we've helped clients with data engineering.
Frequently asked questions
We enforce explicit data contracts using serialization schemas (like Avro or JSON Schema) at ingestion. If a supplier changes their format, the pipeline quarantines the invalid records to a dead-letter queue and alerts the team instead of failing silently.
ETL extracts, transforms, and then loads data, while ELT loads raw data directly into the warehouse and transforms it there. We prefer ELT using dbt (data build tool) and Snowflake/BigQuery because it keeps raw data accessible and runs transformations with cloud-scale performance.
Yes. We build streaming architectures using Apache Kafka or AWS MSK/Kinesis combined with lightweight stream processors, enabling real-time stock updates, checkout telemetry, or instant diagnostic alerts.
Let's Discuss Your Data Engineering Project
Schedule a free 30-minute consultation with a senior engineer. No sales pitch — just an honest assessment of your situation.