CAPABILITIES

Data engineering

Pipelines that do not break at 3am. We design and build batch, streaming, and CDC systems with explicit freshness, reliability, and operability targets.

Streaming pipelines

Batch pipelines

Change Data Capture (CDC)

Capability focus areas

OrchestrationStreamingProcessingStorage

What we do

Workstream 01

Streaming pipelines

Kafka-based streams with exactly-once semantics from ingestion to analytical stores in seconds.

Workstream 02

Batch pipelines

Airflow or Dagster orchestration, dbt transforms, and reliable backfill strategies for scheduled workloads.

Workstream 03

Change Data Capture (CDC)

Debezium, AWS DMS, and custom CDC patterns to sync analytical stores with operational systems safely.

Workstream 04

Data quality and testing

Schema checks and quality gates that catch bad data early, before dashboards and decisions are impacted.

Workstream 05

Operational readiness

Runbooks, lag/error SLOs, and replay strategies so incident response is repeatable instead of heroic.

Workstream 06

Data lake / lakehouse

Iceberg, Delta, and open columnar formats designed for scale while avoiding unnecessary vendor lock-in.

How we work

Audit and architecture

Map existing flows, reliability gaps, and priority consumers to define the right target architecture.

Quick stability wins

Stabilize failing pipelines and add observability around lag, freshness, and error rates.

Core pipeline delivery

Build or migrate streaming, batch, and CDC flows with tested transformations and rollback paths.

Handoff and enablement

Deliver docs, runbooks, and team training so internal engineers can evolve pipelines confidently.

Tech we use

Orchestration

AirflowDagsterPrefectTemporal

Streaming

KafkaRedpandaPulsarKinesis

Processing

FlinkSparkdbtBeam

Storage

ClickHouseSnowflakeBigQueryIceberg

CDC

DebeziumAWS DMS

Languages

PythonGoJavaScala

Featured case study

Proof in production

We replaced nightly ETL with Kafka + ClickHouse, cutting time-to-insight from 24 hours to under 60 seconds.

Read case study

Questions we get

We're a small team. Do we need a complex stack?

Not always. We right-size architecture to your scale; simple systems are valid when they meet your goals.

Kafka or Kinesis?

It depends on ecosystem, scale, and operational constraints. We recommend based on your context.

Do you help migrate from legacy ETL tools?

Yes. Legacy ETL migrations are a common engagement path.

Pipeline problems?

Get a practical data engineering plan tailored to your current stack and team.

Talk to a data engineer