MarTech Lead (Data & AI)

Description

IG has been at the centre of retail trading and investment since 1974, when we helped create the market for financial spread betting. Today, we're a FTSE100 fintech operating across five continents, serving over 700,000 clients and handling billions in transactions - built on decades of scale, trust and proof. We didn't pivot to innovation; it's how we've always operated. What that means for the people who work here is real: genuinely complex problems to solve, the technology and resources to tackle them properly, and the kind of scope that’s rare in established businesses. The bar is high - bring a curious and forward-thinking mindset and we'll give you the platform to define what comes next. Join us at IG – the future gets built here.

The Perks

• Competitive salary

• Flexible Benefits Package on top of your salary (12%)

• Private medical cover for you and your family

• Life insurance

• Contribution to gym memberships

• 25 Days holiday, with 1 additional day off to celebrate your Birthday & 2 additional days off a year for voluntary work (28 in total)

• The option to buy or sell holiday days

• A comprehensive global and local onboarding process

• Enhanced primary (maternity), secondary (paternity), and shared parental pay and leave, as well as a range of support and benefits for parents

How we work

• Lead and Inspire: Drives trust, alignment, and enthusiasm

• Think Big: Focus on the problems that most impact commercial outcomes

• Champion the client: Understand and prioritise client's needs

• Deliver at pace: Push for fast, sustainable growth;

• Raise the bar: Take ownership, be accountable and share feedback

You will likely bring:

• Strong ability to translate business objectives into structured delivery plans, requirements and documentation.

• Clear, confident communication skills with the ability to work effectively across teams with different priorities, disciplines and levels of technical understanding.

• Experience managing multiple initiatives in parallel, including coordination of external vendors and delivery partners, while maintaining focus on outcomes.

• Strong SQL and comfort working in BigQuery or an equivalent warehouse. You can write your own validation queries, not just request them

• Working proficiency in Python (or another mainstream language) for scripting data checks, automation, and pipeline debugging

• Practical experience building with AI/LLM tools. Automating a workflow, building an internal agent, or similar beyond casual chatbot use

• Experience owning (not just contributing to) at least one platform migration or major data integration project

• Comfort designing data architecture, schema decisions, identity resolution, reconciling conflicting data across systems

What you’ll do

• Act as the technical owner of the SFMC → Braze migration: own the plan, sequencing, and cutover strategy, not just a workstream within it

• Personally validate data and journey parity between SFMC and Braze, reconciling records, logic, and event data at the SQL/pipeline level, not just sign-off via a status report

• Identify and fix migration-related data issues directly (mapping errors, duplicate/missing events, broken triggers) rather than logging tickets for someone else to resolve

• Own the technical relationship with Client Tech and Data Architecture for everything migration-related, uptime, data integrity, and rollback/contingency planning

• Define the technical architecture for a unique source of truth, schema design, identity resolution/stitching across tools, update cadence, and ownership of conflicts when tools disagree

• Manage integrations of 3rd-party tools, understanding the technical data contracts involved, spec how data from disparate tools should be integrated, deduplicated, and reconciled into a single, trusted data store.

• Work with Data Architecture and the data warehouse team to align this with the existing BigQuery/attribution setup

• Build validation/monitoring so the source of truth is trusted by default, flag drift or breakage before stakeholders notice bad numbers

• Diagnose and resolve data quality issues directly. Duplicate events, integer overflow/ID issues, broken field mappings, integration failures rather than escalating to a data engineer

• Write and maintain SQL/Python for data validation, QA scripts, and user journey debugging.

• Use AI tools (LLM-based agents, scripting, internal automation) to speed up recurring MarTech work: QA checks, documentation, campaign audits, migration validation, training material generation etc.

• Manage vendor relationships