Job Title
Data Solutions Architect
Job Description
So, who are we?
IG Group is a FTSE 100 fintech operating across five continents, serving over 1.3m customers and handling billions of dollars in transactions – built on 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!
Your role in the Team's Success
You will sit within Enterprise Data, reporting into the data strategy and governance function, and working closely with data and database engineering, messaging and data science teams.
Your remit is solution architecture within the enterprise data standards.
This is a role for someone who wants to design hard systems in a regulated environment with real consequence, and who is looking to grow into enterprise-level scope over time — not for someone who expects to arrive owning it.
What you’ll do
Own the solution architecture and high-level design for Post Trade regulatory reporting data — from trade and lifecycle event capture on the enterprise message bus, through the lakehouse layers, to submission, acknowledgement handling and reconciliation against trade repositories.
Design and deliver solution architecture for assigned domains on the GCP lakehouse — from event ingestion off Kafka, through Bronze/Silver/Gold, to consumption by analytics, operational and regulatory reporting workloads.
Model the data. Dimensional and canonical modelling, conformed dimensions, slowly changing dimensions, grain determination, and the trade-off between nested and repeated structures versus normalised models in BigQuery. Design to the enterprise canonical standard — extending it through the governance forum where a domain genuinely needs something it does not cover.
Own schema contracts in practice. Avro schema design and review; compatibility mode selection (backward, forward, full and their transitive variants) and the producer/consumer upgrade ordering each one implies; union and default-value design for evolvability; topic, key, partition and compaction design; and the operational consequences of getting any of those wrong.
Design ingestion for both batch and streaming. Watermarks, windowing, triggers and allowed lateness for out-of-order arrival; replay and restatement; idempotency and deduplication strategy; dead-letter and poison-message handling; and honest reasoning about where exactly-once guarantees actually stop — they do not extend past the Kafka boundary to an external sink.
Build data quality and profiling into the design — DQ gates at layer boundaries, reconciliation and break detection, completeness and accuracy controls, and the lineage and audit evidence that regulated consumers, internal audit and regulators will ask for.
Apply the governance model. PII classification, BigQuery policy tags, column-level and row-level security, dynamic data masking, IAM/RBAC design, CMEK and key management — including the design tension where privacy obligations and multi-year regulatory retention pull in opposite directions.
Work with infrastructure and platform engineering to specify and guide the setup of the runtimes the data architecture requires.
Produce the artefacts and take them through review. HLDs, interface specifications, ADRs and reference designs, authored to a standard that survives formal architecture governance and your own absence from the room.
Work across teams — with producing service teams on event contracts, with data and database engineering, messaging and data science, and with business and compliance stakeholders on what the data has to mean.
Contribute to the architecture - design reviews, standards feedback,…