Data Engineering Services

A focused engagement — one to three data sources, a single warehouse, dbt transformations, and a BI layer — typically takes six to twelve weeks. Larger stacks with multiple source systems, real-time requirements, and ML feature pipelines take three to six months. We start with a data audit and architecture design (usually two weeks) that produces a written roadmap before any pipeline code is written. You know what you're getting and when before we begin.

It depends on your cloud provider, team's SQL dialect familiarity, and workload type. BigQuery is usually the best choice for variable workloads on GCP — serverless pricing means you pay per query, not for idle compute. Snowflake is better for teams that want compute-storage separation with a consistent multi-cloud option and strong support for semi-structured data. Redshift makes sense if you're deeply invested in the AWS ecosystem and need tight integration with Spectrum or other AWS services. We'll recommend based on your actual situation and give you a cost model for each option.

Yes — reliability remediation is one of the most common engagements we run. We start with a pipeline audit: mapping existing jobs, identifying failure modes, checking for test coverage and monitoring, and reviewing the transformation logic for correctness. The most common issues are untested dbt models, pipelines that run but produce wrong results because there's no data quality assertion, and a lack of alerting on freshness. We fix the foundation first, then help you extend it.

Yes. We build feature stores and transformation pipelines that feed both batch training and real-time inference, on platforms like Feast, Tecton, or custom implementations on top of Snowflake or BigQuery. We work closely with ML engineers — either on your team or our own — to ensure the features produced match what the models were trained on, which is the single most common source of training-serving skew. If you need end-to-end MLOps, we can scope that as a combined data engineering and AI engagement.

A discovery and architecture engagement (audit, data model design, written roadmap) is typically $8K–$15K and takes two to three weeks. Full pipeline builds range from $30K for a focused single-domain pipeline to $150K–$300K for a multi-source, real-time warehouse with a complete dbt transformation layer and BI integration. Ongoing retainers for pipeline maintenance, monitoring, and incremental development run $5K–$12K per month depending on complexity. All engagements start with a written statement of work with fixed milestones.