Job Description
We are looking for a Director – Software Engineering who leads from the codebase outward. This
role is 60% technical- coding, code review, system design, and architecture- and 40%
leadership- people, process, and delivery. If you are looking for a purely managerial role, this is not
it.
You will set the technical direction for the full engineering function, own system-wide architecture
decisions, and be the engineering counterpart to the CPTO on every hard problem. You will be
hands-on in the codebase every week- not as a bottleneck, but because that is how you set
standards and earn credibility with the team.
1Source, 1Data, and 1Xcess- are built on top of the
systems your teams design and operate. Everything that works in production is a credit to your
function.
What You'll Do
- Technical Architecture & System Design
- Own system-wide architecture across all five engineering streams- every major design
decision, from database schema choices to service boundary definitions to API contracts, is
yours to lead and document.
- Lead structured system design reviews for all significant features and platform changes-
producing Architecture Decision Records (ADRs) with explicit trade-offs, not informal Slack
threads.
- Design and evolve a microservices architecture that is loosely coupled, independently
deployable, and operationally simple- avoiding both monolith sprawl and microservice
overengineering.
- Architect event-driven systems using Kafka: define topic naming conventions, partitioning
strategies, consumer group design, schema evolution via Schema Registry, and CDC
patterns with Debezium- and enforce these standards across all teams.
- Own the polyglot stack decisions: Node.js and Rust for services, React/Next.js for frontends,
MongoDB and ClickHouse for data, Kafka for streaming, Apache Camel for integration
routing, and OpenSearch for search- choosing the right tool deliberately, not by default.
- Define integration architecture standards for API integrations, BOT-based data acquisition
pipelines, and ERP/CRM connectors- ensuring every external data flow is reliable,
observable, and recoverable.
- Evaluate new technologies with engineering discipline- prototype, benchmark, document the
trade-off, and only then commit the team.
- Hands-On Engineering (60%)
- Write, review, and refactor production code across the stack- you are a contributor, not just
an approver.
- Conduct architecture reviews and deep code reviews for high-stakes changes- setting the
quality bar through your own output, not just your feedback.
- Personally own and resolve the hardest engineering problems: cross-service consistency,
high-throughput pipeline bottlenecks, cold-start latency on serverless inference paths, and
integration reliability under load.
- Pair with engineers on complex implementations- your presence in the code is how you
transfer knowledge and raise the team's ceiling.
- Stay current across the stack- you can context-switch from a Rust service's memory model
to a Next.js hydration bug to a Kafka consumer lag issue in the same day.
- DevOps, Reliability & Cloud Operations
- Own reliability, security, performance, and cost across all AWS infrastructure- ECS/EKS,
RDS, S3, Lambda, CloudFront, API Gateway, and the services that glue them together.
- Establish and enforce DevOps and SRE practices: CI/CD pipelines (GitHub Actions),
Infrastructure as Code (Terraform or CDK), container orchestration (Kubernetes on EKS),
and on-call rotation.
- Define and track SLOs and error budgets for all production services- and hold teams
accountable to them, not just measure them.
- Build observability into every system from day one: structured logging, distributed tracing
(OpenTelemetry), and metrics dashboards (Prometheus/Grafana or CloudWatch)- alerting
that fires before customers notice.
- Lead incident response: own the runbook culture, drive blameless post-mortems, and
ensure every incident produces a concrete engineering improvement.
- Drive cost discipline on AWS- right-size instances, optimise data transfer costs, benchmark
self-hosted open-source options against managed services, and report on infra spend
monthly.
- Data Engineering & Ingestion Infrastructure
- Partner with the Data Engineering team to ensure the full data lifecycle is production-grade:
raw ingestion from distributor APIs, scraping pipelines, and partner feeds; transformation
and enrichment via Spark/PySpark and dbt; storage in MongoDB, ClickHouse, and
OpenSearch; and delivery of clean, analytics-ready datasets to AI models and dashboards.
- Own the architecture for high-volume, high-frequency data ingestion- the platform ingests
component availability, pricing, and supplier data continuously; pipeline reliability and
freshness are product features, not just engineering concerns.
- Review and guide data model design across MongoDB (operational), ClickHouse
(analytics), and OpenSearch (search)- balancing write performance, query latency, and
schema flexibility for both AI consumption and customer-facing queries.
- Set standards for data quality, validation, and lineage- every dataset that reaches an AI
model or a customer dashboard must have a defined owner, a quality score, and a
freshness SLA.
- Integration Engineering & Data Acquisition
- Work with the Integrations team to ensure the Apache Camel-based integration framework
is scalable, maintainable, and well-documented- covering ERP systems (SAP, Oracle),
distributor APIs, CRM platforms, and market data feeds.
- Set architecture standards for BOT development and web scraping- the integrations team
builds automated data acquisition pipelines that extract component availability, pricing, and
supplier data from distributor portals and public sources at scale; these must be reliable,
stealthy, and rate-limit-aware.
- Define API integration patterns: authentication strategies (OAuth2, API keys, mTLS), rate-
limit handling, retry logic, idempotency, and structured error contracts- enforced consistently
across all third-party integrations.
- Ensure all integration and scraping pipelines are observable end-to-end: every message
flow has a trace, every failure has an alert, and every data acquisition job has a freshness
SLA that is monitored.
- Team Leadership & People Development
- Coach and manage a team of 10+ engineers across four streams- assign work, review
code, set goals, run 1:1s, deliver feedback, hire, and onboard.
- Partner with stream leads (Backend, Frontend, Data Engineering, Integrations) to set
quarterly goals, run effective sprint ceremonies, and maintain delivery predictability without
bureaucratic overhead.
- Create an engineering culture defined by craft, ownership, and psychological safety- where
engineers raise issues early and fix problems permanently.
- Run a lean Agile process that actually helps: shape work with Product, estimate with
honesty, run effective standups and retros, and keep delivery cadence visible to the CPTO.
- Own hiring for the engineering function- define role profiles, run structured interviews, and
build a team that raises the average with every hire.
- Level up engineers through deliberate pairing, code review, and architecture exposure- not
just annual performance cycles.
Required Skills
[You will set the technical direction for the full engineering function, own system-wide architecture decisions, and be the engineering counterpart to the CPTO on every hard problem. Y]
Additional Information
Ideal Candidate Background
We are not filtering by company name- we are filtering by the nature of engineering. The right
candidate has spent the majority of their career building product or platform companies, not
services, consulting, or system integration firms.
We prioritise What that looks like in practice
Product / platform companies B2B SaaS, marketplace, e-commerce, fintech, or logistics tech
companies where engineers own systems end-to-end and are
accountable to uptime, latency, and customer impact- not just
delivery milestones.
Event-driven & microservices
at scale
Companies that run Kafka in production, have decomposed
monoliths into services under real load, and have dealt with
distributed system failures- not just companies that use the right
terminology.
Data-intensive platforms Platforms where data volume, data freshness, and data quality
are product features- pricing engines, catalogue platforms,
analytics products, supply chain systems, or marketplace
intelligence layers.
Integration-heavy systems Experience building API integration platforms, automated data
acquisition pipelines, or BOT-based scraping systems at
production scale- where reliability and freshness of external
data directly affects product quality.
AWS-native engineering Candidates who have designed, deployed, and operated
systems on AWS- not just used it as a hosting layer. Cost
optimisation, networking, IAM, and reliability engineering on
AWS matter here.
We are less interested in Pure services / consulting backgrounds, agency engineering, or
roles where delivery was the primary KPI and system ownership
was handed off after go-live.
