Outsourced Technical Experts for an NYSE-Listed Financial Services Firm
INDUSTRY:
TECH STACK:
- Front end –
React JS and Angular JS - Back end –
JAVA, Springboot framework - Messaging Services –
Kafka, Kafka Streams - Containerisation using Dockers and Kubernetes
- Cloud –
AWS and Openshift
Database- Mongo DB
Executive summary
A global, NYSE-listed financial services firm operating a leading institutional electronic trading platform in fixed-income markets needed to expand engineering capacity without inflating costs or sacrificing quality. Post-Brexit hiring constraints, IR35 cost pressures, and high contractor churn were eroding delivery velocity at precisely the moment the company was scaling new market capabilities across an all-to-all trading marketplace used by more than 2,000 institutions.
Ennovision partnered with the firm to supply outsourced technical experts through a blended model: a mix of local and sponsored specialists in the UK alongside a stable bench of mid- to senior-level engineers from our offshore development centre. We aligned tightly to the client’s stack—React/Angular front-ends; Java/Spring Boot services; Kafka and Kafka Streams; containers on Docker/Kubernetes; cloud on AWS and OpenShift; and MongoDB— and instituted delivery rituals, quality controls and knowledge-retention practices that reduced churn and increased predictability. The engagement produced up to 30% savings in software development and maintenance, preserved resource continuity, and gave the client the ability to ramp up or down quickly without risking critical roadmaps.
Client context
The client operates one of the world’s most respected electronic trading platforms for fixedincome instruments, providing expanded liquidity, improved execution quality and cost efficiencies across global credit markets. Its all-to-all trading marketplace is widely regarded as a preferred venue, with a patented technology stack and a network of 2,000+ asset managers and institutional broker-dealers. Speed, reliability and market integrity are non-negotiable.
While the product vision was ambitious, the talent market in the UK tightened. The client faced higher local consulting costs (amplified by IR35 reforms) and reduced access to permanent hires post-Brexit, which made scaling teams both expensive and slow. Compounding the problem, a high churn rate among contractors created knowledge gaps and rework, undermining delivery momentum.
The challenge
1. Cost inflation for local experts — a 35–40% rise in the cost of hiring local consultants pushed programmes over budget and delayed portfolio commitments.
2. Restricted permanent hiring — post-Brexit rules constrained the UK talent pool, stretching time-to-fill and inflating offers.
3. Churn and instability — traditional staff augmentation produced frequent roll-off/rollon cycles, eroding domain knowledge and adding onboarding overheads.
4. Complex, high-throughput stack — the platform’s real-time and near-real-time workloads (React/Angular UIs, Java/Spring Boot, Kafka Streams, Kubernetes, AWS/OpenShift, MongoDB) demanded engineers who were productive from day one.
Objectives
• Stabilise capacity with a predictable, low-churn team model.
• Reduce run-rate costs while keeping senior engineering capability.
• Preserve velocity across front-end, back-end and data streaming components.
• Enable flexible scaling—grow or shrink squads in weeks without disrupting programmes.
• Embed quality and resilience practices that match the platform’s regulatory and performance stakes.
All objectives needed to be met without diluting accountability or complicating the client’s governance model.
Why a blended delivery model?
Purely local hiring was too costly and slow; purely offshore risked product intimacy and stakeholder proximity. The hybrid approach we proposed balanced both:
• Local and sponsored UK specialists for stakeholder engagement, critical path items, and high-ambiguity problem solving.
• Offshore mid-/senior engineers to provide sustained throughput, reduce single-pointsof-failure, and absorb cyclical demand.
This model aligned to the client’s context and directly addressed IR35 cost pressures and post-Brexit scarcity while maintaining time-zone overlap and standards
Engagement blueprint
Team composition and roles
• Front-end engineers (React, Angular): build and harden trading and monitoring UIs, ensuring accessibility and performance budgets.
• Back-end engineers (Java/Spring Boot): implement microservices, API contracts, and cross-cutting concerns (auth, rate limiting, idempotency).
• Streaming specialists (Kafka/Kafka Streams): design topic hierarchies, schema evolution, and exactly-once semantics where required.
• Platform engineers (Docker/Kubernetes, AWS/OpenShift): IaC patterns, autoscaling, and golden-path deployments.
• Data engineers (MongoDB): schema design for operational workloads, indexing strategies and performance diagnostics.
Governance and cadence
• Two-week sprints with demo-first reviews and definition-of-done including unit/contract tests, security scanning and performance checks.
• Shared backlog in the client’s tooling, with story mapping to make dependencies visible.
• Runbooks and ADRs for key architectural decisions to guard against knowledge loss when team shape changes.
Knowledge retention
• Architecture Decision Records (ADRs) and service playbooks to capture context.
• “You build it, you run it” rotations with clear on-call charters.
• Pairing and code walkthroughs across locations to flatten the learning curve.
Technical alignment to the client stack
Front-end (React & Angular)
• UI consistency & performance via component libraries, linting and bundle-size budgets.
• Observability with client-side telemetry for Core Web Vitals within authenticated experiences.
• Security: OAuth/OIDC flows integrated into SPA routing to protect privileged data.
Services (Java & Spring Boot)
• Contract-first APIs (OpenAPI) with generated clients and test stubs to de-risk integration.
• Resilience patterns: circuit breakers, bulkheads, retries with back-off for peak trading windows.
• Security gates: SCA/SAST in the pipeline; signed artefacts; secrets via a centralised vault.
Streaming (Kafka & Kafka Streams)
• Topic design & governance: naming conventions, retention policies and compacted topics for reference data.
• Schema registry & evolution: backward-compatible schemas with automated compatibility checks.
• Idempotency & ordering strategies to maintain event semantics where trade lifecycle ordering is critical.
Platform (Docker, Kubernetes, AWS & OpenShift)
• Golden images and base charts to standardise deployments across teams.
• Autoscaling & HPA tuning for predictable performance under market spikes.
• Multi-environment parity enforced via IaC for security baselines, policies and resource quotas.
Data (MongoDB)
• Workload-aware schema design to keep hot paths on optimal indexes.
• Ops hygiene: automated backups, point-in-time recovery, and performance dashboards for query hotspots.
Delivery approach
1. Stabilise first — replace ad-hoc resourcing with named, long-tenured engineers; set clear definition of done and quality bars per component.
2. Standardise the path to production — shared pipelines for build/test/scan/deploy; Kubernetes release templates; Kafka topic governance.
3. Instrument and learn — observability baked into every service; SLOs for latency and error budgets on critical flows.
4. Iterate with business proximity — local engineers embedded with product owners; offshore squads swarm backlog items that benefit from parallelisation.
5. Flex capacity safely — agreed ramp-up/ramp-down windows and cross-training matrices to preserve delivery continuity.
Risk management
• Churn risk — mitigated with knowledge artefacts (ADRs/runbooks), pairing, and rotation policies; supplier-side attrition buffers.
• Throughput risk — parallelised work with clear API contracts; used contract tests to keep consuming teams unblocked.
• Regulatory/security risk — automated checks in CI, dependencies vetted, and leastprivilege defaults on clusters and cloud accounts.
• Cost risk — predictable pricing for core team; elastic capacity pre-approved to handle market events without costly emergency hiring
What changed: before and after
| Dimension | Before | After |
|---|---|---|
| Cost | Rising local consulting spend; unplanned overages | Up to 30% savings on development & maintenance run-rate |
| Capacity | Sporadic, dependent on local availability | Elastic teams with pre-vetted offshore engineers |
| Continuity | High churn, loss of context | Low churn, named engineers, codified knowledge |
| Velocity | Delays due to hiring lead time | Faster throughput via blended squads and parallelised delivery |
| Governance | Inconsistent standards between teams | Standard pipelines and templates, contract-first APIs |
Outcomes
1) Quantified savings without quality compromise
The engagement delivered up to 30% savings across software development and maintenance while maintaining seniority and stack proficiency—a direct response to cost inflation in the UK market.
2) Certainty and predictability
Churn fell to a bare minimum, giving product managers reliable velocity and fewer reinductions. Resource visibility improved, enabling quarterly planning grounded in actual capacity rather than wishful hiring assumptions.
3) Agility on demand
With an offshore bench of mid-level engineers already aligned to the client’s practices, the firm could scale up rapidly for critical releases or regulatory deadlines, then scale down without organisational whiplash.
4) Stronger engineering discipline
Common templates, pipelines, and contract-first APIs reduced integration defects and enabled parallel workstreams across front-end, back-end and streaming services— particularly valuable in Kafka-heavy domains.
Lessons for financial-services technology leaders
1. Blend proximity with scale — use local experts for ambiguous, stakeholder-dense problem spaces; anchor throughput and stability with an offshore bench that shares your standards.
2. Institutionalise knowledge — ADRs and service runbooks are your hedge against churn.
3. Make contract tests your currency — in distributed platforms, consumer-driven contracts keep teams moving even when providers evolve.
4. Treat the platform as a product — golden images, base charts and guardrails reduce cognitive load and error rates across squads.
5. Plan elasticity up-front — pre-approve ramp mechanisms and onboarding paths so surge moments don’t become cost shocks.
Roadmap (illustrative)
• Observability unification — a single pane of glass for logs/metrics/traces across AWS and OpenShift clusters, mapped to business SLOs.
• Event governance at scale — automate Kafka schema compatibility checks and lineage dashboards; expand self-service topic provisioning with guardrails.
• Performance engineering — budget-driven optimisation of React/Angular bundles and Spring Boot cold-start paths; systematic load-testing for peak trading windows.
• Developer productivity — template repositories with paved-road CI, local dev containers, and security scanning defaults.
• Skills laddering — a formalised pathway from mid-level to senior roles within the blended team to sustain low churn and deepen ownership.
Why Ennovision
Ennovision combines cloud engineering, application engineering and quality assurance with a pragmatic resourcing model. For this client we delivered outsourced technical experts matched to a demanding technology stack—React/Angular, Java/Spring Boot, Kafka/Kafka Streams, Docker/Kubernetes, AWS/OpenShift, MongoDB—and wrapped them in process discipline that made outcomes predictable. The result was a stable, scalable and cost efficient extension of the client’s engineering organisation, aligned to the realities of today’s UK and global talent markets.