Do you build on our cloud accounts?

Yes. Everything runs in your environment with your security controls, and you own it from day one.

How do you keep pipeline costs under control?

Incremental processing, right-sized compute, partitioning, caching and cost monitoring per pipeline, with alerts before a job becomes a budget problem.

Can you support both batch and streaming?

Yes. We design a unified architecture so the same models and governance serve both, which avoids two sources of truth.

How do you guarantee the numbers match after a migration?

Parallel runs with automated reconciliation at row and aggregate level for every migrated dataset, signed off by the business owner before cutover.

Do we really need streaming, or is faster batch enough?

Often faster batch is enough, and it is simpler. We help you decide based on the decision latency actually required, not on fashion.

Data Science & Engineering

Data Engineering & Platform Modernization

Batch and streaming pipelines, lakehouses and migrations off legacy warehouses.

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See related work

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Overview

Data engineering is the plumbing nobody notices until it fails. Reliable pipelines, well-modeled data and platforms that scale are what make every dashboard accurate and every model trainable. We build that plumbing with the discipline of software engineering: version control, tests, observability and clear ownership.

Our engineers work across batch and streaming, cloud warehouses and lakehouses, structured and unstructured data. We favor open formats and standard tools so your team can run and extend what we build.

This page also covers platform modernization off legacy warehouses and real-time or streaming data pipelines.

Offering 1

Data engineering and pipelines

Reliable pipelines, well-modeled data and platforms that scale are what make every dashboard accurate and every model trainable. We build that plumbing with the discipline of software engineering.

Pipeline development

Ingestion, transformation and orchestration for batch and near-real-time data across sources.

Lakehouse and warehouse builds

Medallion and dimensional models on Databricks, Snowflake, BigQuery and open table formats.

Data modeling

Business-aligned models, semantic layers and metrics definitions that survive tool changes.

Data quality and observability

Automated tests, freshness and volume monitoring, lineage and alerting built into every pipeline.

Typical use

Consolidating data from ERP, CRM, product and marketing systems into one governed platform
Feeding machine learning and LLM applications with clean, current data
Replacing fragile scripts and manual extracts with monitored pipelines

Offering 2

Data platform modernization

Legacy warehouses and hand-built ETL struggle with volume, variety, cost and the demands of AI. Modernization moves your data estate to a cloud platform that scales on demand and costs what you use.

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Migration assessment and planning

Inventory of tables, jobs, reports and consumers; dependency mapping; effort and cost estimation.

Warehouse and ETL migration

Moving schemas, pipelines and logic from Teradata, Oracle, SQL Server, Hadoop and legacy ETL tools to modern platforms.

Automated conversion and validation

Tooling-assisted code conversion with row-level reconciliation between old and new.

Cost optimization and FinOps

Workload tuning, storage tiering and monitoring so the cloud bill stays predictable.

Typical use

Retiring an on-premises warehouse before a hardware or license renewal
Consolidating several regional warehouses into one governed platform
Moving from Hadoop clusters to a managed lakehouse

Offering 3

Real-time and streaming data

Some questions expire in seconds: is this transaction fraudulent, is this machine about to stop, where is this shipment right now. Streaming analytics answers them while the answer still matters.

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Event streaming platforms

Kafka, Kinesis and Pub/Sub architectures with schema registries and governance.

Stream processing

Real-time transformations, joins, aggregations and detection with Flink, Spark Structured Streaming and ksqlDB.

Change data capture

Streaming database changes into platforms and applications without batch lag.

Real-time dashboards and alerts

Live operational views and threshold or anomaly-based alerting.

Typical use

Fraud and anomaly detection on transactions and telemetry
Live logistics tracking, ETA updates and exception alerts
Manufacturing line monitoring and predictive alerts

Want this for your product? Talk to an engineer who has built it.

Book a working session

How we work

How we deliver

01
Assess
Sources, volumes, quality issues, consumers and the platform decisions already made.
02
Design
Ingestion patterns, models, orchestration, testing and cost controls.
03
Build
Pipelines delivered domain by domain with tests, documentation and monitoring.
04
Operate
Handover with runbooks, or ongoing operation as a managed service.