Hire a Data Engineer

Most teams need solid batch first: reliable nightly or hourly loads into a clean warehouse cover the large majority of analytics and reporting. Streaming is worth the extra complexity only when a real decision cannot wait, live fraud checks, operational dashboards, or user-facing features. On the call we look at your actual latency needs so you do not pay for streaming you will not use.

It depends on your data volume, team skills, and cloud. Snowflake and BigQuery are strong managed warehouses for most analytics; Databricks and a lakehouse fit heavier data science and large unstructured data. We assess what you already run and what your workloads need, then recommend one, rather than pushing a favourite.

For a scoped source landing in a clean, tested set of tables, a first pipeline is usually running within the paid one-week trial. Broader platform work, many sources, streaming, or a full remodel, takes longer, but we sequence it so you get a trustworthy first slice early instead of waiting months for everything at once.

Every model ships with tests: not-null, uniqueness, accepted values, and referential checks, plus freshness and row-count monitors on the pipeline. Bad or late data raises an alert and, where possible, is quarantined before it reaches a dashboard or a model, so problems are caught at the source, not by a confused stakeholder.

A senior data engineer sets it up and your team runs it: column-level lineage so you can trace any number back to its source, a catalog with owners and definitions, and access controls that fit your compliance needs. The goal is data people can find, trust, and use without asking around.

NDA and IP assignment are signed before onboarding and everything produced belongs to you, including trial work. If the fit is wrong, Devlyn provides a free replacement with a documented handover. Engagements are planned with overlap for your working hours, in English, every day.