Lead Data Engineer

Working at Create Future
Create Future is an AI-native consulting partner where people do work that matters and are supported to do it well. We work alongside organisations such as Pay Pal, adidas, Nat West, Fan Duel and Money Saving Expert, building digital products and services that make a difference while always putting people first.

We’re a team of creators. We write code, shape delivery, build go-to-market strategies, develop AI solutions and create the practices that support our people. We work side by side with our clients, challenging what’s not working and helping them to build the

We're looking for an experienced Lead Data Engineer to join our growing Data Practice, working on a Financial Services engagement centred on a large, long-lived SQL Server estate. You'll work as part of a multidisciplinary team helping a major FS client understand and de-risk a database landscape that has grown, unchecked, over 15years of trading activity.

This is a role for someone who is comfortable going in without a map: a single SQL Server RDS instance holding around 1,100 tables and fifteen years of trade data, with no archiving strategy, no inventory of stored procedures, and tables duplicated across multiple schemas with no reliable way to tell what's actually in use.

Part of the engagement's as-is analysis uses Claude to scan the codebase, map integration points, and flag likely dead code — you'll sit at the centre of that process, validating, interrogating, and making sense of what the tooling surfaces.

As a Lead, you'll shape the technical approach to the estate discovery, mentor engineers supporting the analysis, and act as a trusted, credible partner to the client's data and platform teams.

This role is Hybrid - requires 2 days per week on our clients site in Glasgow.

• Leading the as-is analysis of a ~1,100-table SQL Server RDS estate — establishing what exists, how it'sstructured, and where the risk sits, in the absence of any existing documentation or stored procedure inventory.
• Working alongside Claude-driven codebase scanning to map integration points, trace data lineage, and identify likely dead code, then applying engineering judgement to validate and prioritise the findings.
• Investigating duplicated tables across schemas to determine which copies are live, which are redundant,and what depends on each — building the tooling and queries needed where none currently exist.
• Designing an approach to 15 years of unarchived trade data — assessing volume, growth, and usage patterns to inform archiving, retention, and future-state options.
• Producing clear, defensible documentation of the database estate — schemas, dependencies, stored procedures, and data flows — that the client can rely on long after the engagement ends.
• Advising on remediation and modernisation options once the estate is understood, from consolidation and archiving through to longer-term platform or cloud migration.
• Leading and mentoring a small team of data engineers on the engagement, setting standards for how findings are verified, tested, and documented.
• Acting as a credible, hands-on technical partner to client stakeholders — including DBAs, platform owners, and risk/compliance contacts — who will be understandably cautious about changes to a live trading data estate.

You know your way around legacy SQL Server estates

• You're genuinely strong in SQL Server — schema design, query performance, stored procedures,indexing — and comfortable working in large, poorly documented production estates.
• You've reverse-engineered a database landscape before: worked out what's live vs. dead, resolved duplicated or conflicting schemas, and built confidence in an estate nobody fully understood.
• You understand the particular pressures of financial services data — long retention requirements, auditand regulatory expectations, and the need to prove data integrity for trade and transaction records.
• You're comfortable with ambiguity and incomplete information, and know how to build a reliable picture of a system methodically rather than guessing.

You're comfortable using AI tooling as part of the analysis

• You're open to and…