Data Solutions Architect

IG Group Bangalore, India

Posted 11 hours ago Permanent Competitive

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, knowledge sharing, and mentoring engineers into architectural thinking.

What you'll need for this role

10+ years in data engineering and data architecture, with hands-on delivery of cloud data solutions from design through to production, and demonstrable ownership of at least one end-to-end data solution in a regulated environment.

Post Trade or regulatory reporting data experience. Direct delivery experience on at least one of MiFIR, EMIR (including Refit), ASIC, MAS, SFTR or CFTC reporting. Reportable-event determination, lifecycle and correction semantics, identifier management (UTI, UPI, LEI, ISIN), pairing and matching, reconciliation against trade repository acknowledgements and rejections, back-reporting, and a working grasp of what a completeness or accuracy breach actually costs to remediate.

Deep, hands-on GCP. BigQuery is the centre of gravity: partitioning and clustering strategy, slot reservations and cost governance, authorised views and datasets, materialised views, table snapshots and time travel, the Storage Write API, policy tags, column-level and row-level security, dynamic data masking, and CMEK. Plus Cloud Composer/Airflow (idempotent task design, backfill and catch-up semantics), Dataflow or Dataproc, Datastream for change data capture, GCS, Cloud Run and Cloud Functions, Pub/Sub, IAM, VPC Service Controls, and Dataplex for classification, data quality and lineage. GCP depth is what we are hiring for; breadth on other clouds is welcome but does not substitute for it.

Medallion lakehouse design in production - Bronze/Silver/Gold with immutability at Bronze, progressive quality enforcement, deterministic reprocessing, and a defensible position on where transformation logic is allowed to live.

Kafka at enterprise scale with a governed schema registry (Cloudera, Confluent or equivalent) - Avro schema design and evolution, compatibility modes and their operational consequences, event-driven ingestion into a cloud data platform, consumer group and lag management, compacted topics and tombstone semantics, and the ordering and delivery guarantees Kafka does and does not provide.

Data governance in a regulated environment - IAM/RBAC frameworks, PII classification and encryption, data retention design, audit logging, and compliance-driven design under FCA, GDPR or equivalent supervision.

Architecture artefacts that have passed formal review - HLDs, ADRs, interface specifications, reference designs. You can evaluate competing options, articulate trade-offs without hedging, decide under ambiguity, and write the decision down.

Cloud integration patterns - REST API, MFT, event streaming, cross-cloud AWS↔GCP - including Workload Identity Federation, VPC perimeters, and JWT-based service-to-service authentication.

Working command of core data platform concepts - data lake, ODS, data warehouse and lakehouse - and a clear view of when each is the right answer rather than a default.

Stateful stream processing with Apache Flink or Spark Structured Streaming - state stores, checkpointing, and event-time processing at scale.

Enough Java and event-driven / microservices fluency to review service designs and challenge engineering teams credibly. This is not a hands-on development role.

Oracle RDBMS, PostgreSQL and MongoDB, in the context of a legacy estate still carrying business-critical data.

dbt, Terraform or equivalent infrastructure-as-code.

Person specification

A collaborator who works across IT and business teams to realise integration design and evolve the strategy jointly rather than in isolation.

Sound, pragmatic judgement - balancing pace, risk and commercial value to reach decisions that are well informed, rigorous and actionable.

Builds credibility and influence with stakeholders through technical substance and clear communication.

A good facilitator - draws out problems, builds consensus, and secures commitment from the people who have to deliver.

Curious and forward-thinking, with the appetite to grow scope over time.

How we work

We try to take a thoughtful approach to our ways of working as a company. We follow a hybrid working model with 3 days in the office - which we think balances the need to collaborate effectively and connect with each other. When it comes to how we deliver, there are 5 things we want everyone to do to drive high performance, better learning and career satisfaction:

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

We believe that diversity is vital to success, it fuels creativity, drives innovation and sets us up for global success. We're committed to building teams with a variety of perspectives and skills to help us realise our vision and strategy, that's why we encourage applications from people with diverse backgrounds and experiences to join us on this journey. Learn more about our D&I approach here.

The Perks

Your growth fuels our success! Thrive with tailored development programs, mentoring opportunities with leaders, and clear career progression. Expand your network through committees, sports and social clubs. Enjoy extra time off for volunteering and community work.