Build scalable, governed data platforms that turn raw operational data into trusted, analytics-ready insights. Pipelines, warehouses, and governance — engineered for your cloud and your scale.
Most teams have more data than ever — and less confidence in it. Our data engineering services turn raw operational data into a single governed source of truth, automating ingestion and transformation so your analysts and decision-makers spend less time wrangling pipelines and more time acting on what the data is telling them.
From integration to warehousing, we cover the full lifecycle of building and operating modern data platforms.
Unify data from databases, cloud storage, APIs, IoT devices, and SaaS platforms into a single trusted source.
Automated ETL/ELT pipelines built with Apache NiFi, Talend, and Airflow — batch and streaming, with monitoring and alerting baked in.
Modern cloud warehouses on Snowflake, BigQuery, Redshift, and Azure Synapse — designed for analytics-ready, AI-ready data.
Quality, lineage, governance, and security across the lifecycle — so the numbers in your dashboards are the numbers you trust.
All operational data flowing into one governed lakehouse — no more conflicting reports from disconnected systems.
Auto-scaling cloud compute that handles 10× growth without rearchitecture or surprise bills.
Spark and streaming pipelines for real-time event processing, CDC, and large-batch transformations.
Schema validation, anomaly detection, PII masking, and full lineage — compliant with GDPR, HIPAA, and SOC 2.
AWS, Azure, and GCP-native services where they fit — combined with cloud-agnostic tools like dbt and Airflow.
Audit existing data sources, schemas, volumes, and stakeholders. Map current pain points and define measurable outcomes.
Design the target data platform — warehouse vs lakehouse, batch vs streaming, governance model, and cloud topology.
Build pipelines, deploy infrastructure, implement quality and lineage checks, and load priority data domains.
Tune query performance, control compute costs, and run the platform — with optional ongoing managed operations.
Bring us your dashboards, your spreadsheets, and your bottlenecks. We will give you a no-strings opinion on the fastest path forward.
Book a ConsultationWe pick what fits your workload — not what we are vendor-incentivised to sell.
Tailored data architectures and compliance patterns for the verticals we serve most often.
EHR integration, patient data unification, and HIPAA-compliant analytics platforms.
IoT sensor pipelines, supply chain visibility, and predictive maintenance models.
Real-time fraud detection, regulatory reporting, and risk analytics on streaming data.
Network event ingestion, customer 360 views, and churn prediction at scale.
Customer data platforms, inventory analytics, and demand forecasting pipelines.
Smart-grid telemetry, predictive maintenance, and consumption analytics.
Without engineered data pipelines, dashboards lag, ML models fail in production, and teams rebuild the same logic in every tool. Data engineering turns raw operational data into a trusted, governed, analytics-ready foundation that every downstream team can rely on.
ETL tools like Apache NiFi and Talend, big-data platforms like Spark and Hadoop, cloud warehouses on AWS, Azure, and GCP, and SQL/NoSQL databases — chosen to fit your existing stack and skill set, not a one-size-fits-all template.
Operational databases (Postgres, MySQL, Oracle, SQL Server), cloud storage (S3, GCS, Blob), SaaS APIs (Salesforce, HubSpot, NetSuite), IoT devices and event streams, and third-party file feeds. If it produces data, we can ingest it.
A foundation build (warehouse + 3–5 priority pipelines + governance) is typically 8–14 weeks. Larger programmes with full domain coverage and self-service analytics take 4–6 months. We phase delivery so you see value before the full build is done.
Elastic scalability, pay-per-use cost models, global accessibility, managed services that reduce operational overhead, and faster time-to-value compared to on-premise warehouses.
Yes — DataOps managed services covering pipeline monitoring, SLA management, schema-change handling, cost optimisation, and quarterly roadmap reviews.
