Data services. The foundation your business runs on.

Qualified shortlist within 48 hours of a clear role brief. Pre-vetted candidates with confirmed stack experience (Databricks, Snowflake, Fabric, Informatica, Tableau), background checks, and availability, not a pile of résumés. Specialized roles can take longer; we’ll flag it up front.

No. A lot of our data customers start with us on a specific gap (a hard-to-fill role, a stalled migration, a governance program nobody’s owned) while keeping their existing partners in place. We’re comfortable being a second or third vendor, and over time, some customers consolidate with us based on results. The choice is yours, and we’d rather earn the relationship than demand it.

Yes. A lot of our data work starts with individual hires and grows into full program teams when the scope gets bigger than a job description: estate modernization, governance build-out, analytics platform delivery. When that happens, we build a purpose-built team, agree on outcomes and milestones upfront, and own the delivery, so you’re not managing contractors, you’re managing progress. If you already know you need the full team from day one, we can start there too.

If a contract placement isn’t performing in the early weeks, we replace them at no cost. For direct hires, we stand behind our work with a replacement guarantee, with specific terms agreed at engagement scoping. Most of our energy goes into getting it right the first time, which is why we lead with a 48-hour qualified shortlist instead of a pile of résumés. Misses are rare, and when they happen, we own them.

Two ways. First, we invest in the talent pool itself: continuous upskilling on Databricks, Snowflake, Fabric, Informatica, Tableau, and the governance frameworks (data lineage, quality, lifecycle) that matter, with recruiters who actually speak the language of data engineering. Second, we co-invest with our customers. When you’re standing up a governance program or making your data AI-ready, we bring the people who can help you get there, not just fill the seats.

Data estate modernization is the process of migrating legacy data systems (databases, warehouses, pipelines) to cloud-native or hybrid platforms that perform better, scale further, and cost less to run. It’s the foundation every analytics and AI use case sits on.

A data assessment runs 4-6 weeks. Estate modernization spans 3-12 months. Governance build-outs run 3-6 months. Analytics activation is continuous. We scope tightly upfront so you know what you’re committing to before we start.