Skip to content

AI-ready data foundations · Production DataOps & pipeline engineering · Fractional data leadership · CI/CD for data pipelines · Schema drift, caught before it ships · RAG-ready semantic layers · Infrastructure as code · North America, remote-first

Book an audit

Data engineering service

Production DataOps and Pipeline Engineering

Production DataOps turns fragile data pipelines into version-controlled, tested, observable infrastructure with automated delivery and safer schema changes.

Book a data & AI-readiness audit

When you need it

Where this service fits

  • A pipeline depends on manual scripts, undocumented steps, or one person's memory.
  • Schema changes reach dashboards or downstream consumers before anyone can respond.
  • Cloud spend and pipeline reliability need to be managed as engineering concerns.

Audience and problems

Built around the current data reality

For teams like

  • Data teams operating warehouses, lakehouses, and production pipelines at startup or scale-up speed.
  • Engineering leaders who need a stronger delivery and reliability baseline for data work.

Common problems

  • Changes are not versioned, reviewed, or tested like application code.
  • Schema drift and freshness failures are discovered by downstream consumers.
  • Observability and cloud-cost signals are too weak to guide tradeoffs.

Outputs

What the work produces

Outcomes

  • A more repeatable delivery path for pipeline and schema changes.
  • Automated checks for data freshness, quality, and compatibility.
  • Clearer reliability and cost signals for the data platform.

Deliverables

  • Data-as-code and version-control patterns.
  • Automated data-quality and freshness testing.
  • CI/CD workflows for schema migration and pipeline delivery.
  • Observability and cloud-cost optimization review.

Engagement process

A practical path from current state to next step

  1. 01

    Baseline

    We map the current delivery path, dependencies, failure modes, and operational signals.

  2. 02

    Harden

    We introduce versioned changes, automated tests, and CI/CD checks around the highest-risk paths.

  3. 03

    Operate

    We leave the team with observable workflows and practical standards for ongoing reliability and cost decisions.

Tools and platforms

Work with the stack you have

  • dbt
  • Apache Airflow
  • Terraform
  • GitHub Actions
  • Snowflake
  • Databricks

Verified client proof

Hiring Aeolus consultants was easily the best decision given how much experience they brought to our growing team.
Pat Waltz, Product Manager, Jiga
We highly value Aeolus Data Solutions' professional recommendations when we migrated from GCP to Databricks. They evaluated the requirements and growth projections before advising.
Julia Santos, Data Engineering Manager, Zensors

Questions

Frequently asked questions

What does production DataOps include?

It includes data-as-code, automated testing, CI/CD for schema migrations and pipeline changes, observability, and cloud-cost optimization.

Can you work with an existing warehouse or lakehouse?

Yes. The work is scoped around the current environment, whether it uses Snowflake, Databricks, or another part of the modern data stack.

How do you start a pipeline engineering engagement?

Every engagement starts with a short, fixed-fee data & AI-readiness audit so the delivery path can be scoped against the actual environment.

Keep exploring

Book a data & AI-readiness audit