Data Infrastructure & Integrations
What we do
Data infrastructure is plumbing. When it works, no one thinks about it. When it fails, every downstream system - analytics, ML models, customer-facing features - fails with it. We engineer data systems for reliability at volume: ingestion pipelines that handle schema changes and API rate limits gracefully, event-driven architectures that process millions of messages with exactly-once semantics, and orchestration layers that manage dependencies across dozens of data sources. Every pipeline ships with monitoring, alerting, and runbooks so your team can diagnose failures independently.
Real-Time Streaming & Event Architecture
Apache Kafka, AWS Kinesis, and Google Pub/Sub pipelines for real-time data movement between systems. Event schemas with Avro or Protobuf, consumer group management, exactly-once processing, and dead-letter handling. Lakehouse integration with Apache Iceberg or Delta Lake for unified batch and streaming.
API & SaaS Integrations
REST and GraphQL connectors for Salesforce, HubSpot, Stripe, Shopify, and hundreds of SaaS platforms. We handle OAuth flows, pagination, rate limiting, webhook processing, and change data capture to maintain real-time synchronisation between operational tools and your data warehouse.
Data Quality & Observability
Automated quality checks validating freshness, uniqueness, referential integrity, and business rules at every pipeline stage. Data cataloguing, lineage tracking, and PII classification to support GDPR, HIPAA, and SOC 2 compliance. Schema contracts between producers and consumers prevent upstream changes from breaking downstream systems.
How we work together
System Mapping & Data Contracts
We catalogue every data source and destination, identify integration points, and define contracts between systems - field mappings, transformation rules, freshness SLAs, and schema evolution policies. This documentation eliminates the undocumented, brittle integrations that become maintenance nightmares at scale.
Pipeline Engineering & Integration
Custom connectors and ingestion pipelines built with idempotent processing, exponential backoff, and schema validation at entry. For standard SaaS sources we use Fivetran or Airbyte. For non-standard APIs, event streams, and CDC requirements we build custom using Python, Node.js, and cloud-native services.
Orchestration & Deployment
Pipeline orchestration with Airflow, Dagster, or AWS Step Functions - dependency management, parallelism, timeout handling, and dead-letter queues for failed records. Infrastructure provisioned via Terraform for reproducibility. Every deployment is versioned and rollback-capable.
Observability & Reliability
Monitoring dashboards tracking pipeline health, data freshness, row counts, and error rates. PagerDuty or Opsgenie alerts with runbooks that enable independent diagnosis. Data quality checks using Great Expectations or dbt tests validate freshness, uniqueness, and referential integrity at every stage.