Pipelines that run without babysitting.
Batch, streaming, real-time — we build data pipelines that run reliably at scale. Proper error handling, monitoring, idempotent processing, and the ability to replay from any point. Your data team builds features, not fixes broken jobs.
Pipeline uptime across all clients
Reduction in pipeline incidents
Average data freshness (was 6 hours)
Production pipelines built per quarter
What we build
Pipelines built for production.
Batch & ELT pipelines
Scheduled pipelines that extract, load, and transform data reliably. Incremental processing, schema evolution handling, and proper backfill support. Built with dbt, Spark, or SQL — whatever fits your stack.
Streaming pipelines
Real-time data processing with Kafka, Flink, or Spark Streaming. Sub-second latency for event-driven architectures. Exactly-once semantics, late data handling, and windowed aggregations.
Orchestration
Airflow, Dagster, Prefect — we set up orchestration that makes pipelines observable and manageable. DAG design, dependency management, alerting, and automated retries.
Data quality checks
Quality gates at every stage of the pipeline. Schema validation, statistical anomaly detection, freshness monitoring. Bad data gets caught at ingestion, not in the board meeting.
Replay & recovery
Idempotent pipelines that can replay from any point without duplicates. When something breaks — and it will — recovery is a single command, not a weekend incident.
CDC & event capture
Change data capture from databases, APIs, and SaaS platforms. Debezium, Kafka Connect, custom CDC — we capture changes without impacting source system performance.
Sound familiar?
Pipeline problems we solve every week.
“Our data team spends 80% of their time fixing broken pipelines.”
We redesign for reliability — idempotent processing, automated retries, schema evolution handling, and proper alerting. Your engineers go back to building new pipelines, not firefighting old ones.
“Reports show yesterday's data. The business needs real-time.”
We build streaming ingestion alongside your batch layer. Critical dashboards get sub-minute freshness, heavy analytics stay on batch. Best of both worlds, pragmatic cost.
“A single pipeline failure cascades and breaks everything downstream.”
We implement circuit breakers, dependency-aware orchestration, and data quality gates. When one pipeline fails, downstream consumers get clear signals and fallback behavior — not silent bad data.
Tech stack
Tools we use in production.
Ready to build
Let's build pipelines that just work.
45 minutes with our data engineers. We'll review your pipeline architecture, identify reliability gaps, and outline a plan to get your data flowing without the 3 AM pages.
Data engineering projects we delivered
