--- title: "How To Choose A Data Engineering Company: Seven Questions" url: https://www.axelerant.com/blog/how-to-choose-a-data-engineering-company published: 2026-09-24T09:00:00Z author: "Axelerant" source: Axelerant Thinking --- # How To Choose A Data Engineering Company: Seven Questions > Seven questions to ask a data engineering company before you sign, from failure design and legacy retirement to activation and metrics. Search for a data engineering company and you will find hundreds of firms listing the same logos: Snowflake, Databricks, Kafka, Airflow, AWS. The stack is not what separates them. What separates them is whether they have fixed a data problem like yours before, and whether their work keeps running after they leave. This guide covers the questions we would ask if we were hiring a data engineering company ourselves, drawn from the problems our clients bring us and the mistakes we have seen other programs make. ## 1. Can they name your problem before they name a tool? A good partner starts with the business symptom. "Marketing and sales count customers differently." "Reporting takes days." "Consent broke our segments." "Upstream releases break our dashboards every sprint." If the first meeting is a platform pitch, the platform is what you will get, whether or not it fits. Ask them to describe, in plain language, the last engagement where they traced a symptom like yours to its root cause. ## 2. Have they worked at your scale and complexity? Data engineering at twelve sources is a different job from data engineering at two. One organization we worked with ran a regional ERP, a CRM, two commerce platforms, a payments provider, a custom booking platform, a membership system, several content sites and a data lake. The work was less about moving data and more about agreeing on one canonical customer and a contract for every source. At the other end of the spectrum, we built a Kafka-streamed pipeline for a real-time sports platform that went from a handful of messages a day to millions per second at peak, with at-least-once delivery and full observability through Confluent Cloud and Grafana. Ask a prospective partner for examples at both ends of your range. ## 3. Do they build for failure, not just for the happy path? Most data incidents are not dramatic. They are silent: a dropped message, a duplicated record, a late file nobody noticed. Ask how the company designs for that. - Retry paths and dead-letter queues for asynchronous processing. We explain our approach in [Using AWS SQS With Dead Letter Queues For Enhanced Reliability](/blog/using-aws-sqs-with-dead-letter-queues-for-enhanced-reliability). - Data contracts and schema tests between source systems and the warehouse. - Lineage and quality monitoring, not just dashboards. - Consumer monitoring for streaming systems, so lag is caught before users feel it. See [Monitoring And Optimizing Kafka Consumers For Performance](/blog/monitoring-and-optimizing-kafka-consumers-for-performance). ## 4. Will they retire things, or only add them? A mature data engineering company removes as much as it builds. For Red Hat, embedded Axelerant data engineers decommissioned legacy batch and streaming services, retiring more than 25 custom Python jobs and more than 150 Kafka topics, and consolidated onto modern platforms. Every retired job is one less thing to monitor, pay for and explain. Ask for a concrete example of a legacy system they shut down, and how they kept reporting running during the cutover. ## 5. Do they connect the data layer to activation? Data that only sits in a warehouse is potential. The return shows up when it powers a campaign, a personalized experience or a product decision. That means the partner needs to understand identity resolution, consent and the CDP or activation layer, not just ingestion. In one engagement, the choice between two CDP platforms only became clear once we mapped activation requirements back to the data layer. Our article on [why identity resolution projects fail after delivery](/blog/why-identity-resolution-projects-fail-despite-competent-delivery) covers the sequencing mistakes that make this go wrong. ## 6. How do they work with your team? Three engagement models are common, and a good data engineering company will be honest about which one fits. - An assessment or architecture review, when you need agreement on the problem and the plan. - Embedded data engineers working inside your team's rituals and tools, when the plan is clear and you need capacity. - A delivery team that owns a defined outcome, such as a warehouse migration or a streaming pipeline, end to end. Ask who will actually do the work, how many hours of overlap you will get with their team, and how knowledge transfers to your engineers before they roll off. ## 7. What will they commit to measuring? Be wary of any company promising to "transform your business." A data engineering partner controls the outputs: pipeline reliability, time to trusted answer, run cost, records resolved, time to launch an audience. Those outputs ladder into your business case, which you own. Ask what they will report weekly, and what the first measurable fix will be. ## A short checklist - They described your problem before recommending a platform. - They showed work at a scale similar to yours. - They explained how their pipelines fail safely. - They gave an example of something they retired. - They connected the warehouse to activation. - They named the engagement model and the people. - They committed to operational metrics from week one. ## How Axelerant approaches this Axelerant has delivered engineering work since 2005 and has been remote-first since 2012, with 150+ people working across time zones alongside client teams. Our [data engineering services](/what-we-do/engineering/data) cover event streaming, reliable async processing, warehouses and lakes, CDP implementation, first-party data and data strategy consulting. If you are comparing partners, start with a data architecture review: we will map your sources, name the failure points and agree the first fix before any larger build. ![Seven-gate evaluation flow for choosing a data engineering partner, from defining the business symptom through measurable first 90-day outputs](/__l5e/assets-v1/f19ac7ac-e4e9-4057-84ca-9e5a586f1e28/data-engineering-partner-evaluation-v2.svg) --- Read on the web: https://www.axelerant.com/blog/how-to-choose-a-data-engineering-company