ClickHouse Engineer

Reactive Markets; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

Reactive Markets in Singapore is hiring a ClickHouse Engineer to own and operate a multi-datacentre data plant underpinning compliance, billing, and analytics. The estate handles billions of rows daily and requires disciplined production operations.

You will design schema and storage, optimize ingestion and queries, manage capacity, ensure reliability, and collaborate with a senior team across time zones using Kubernetes and Python-based pipelines for real-time analytics.

Responsibilities

• Cluster operations at scale — running, upgrading and evolving a multi-AZ ClickHouse estate on Kubernetes.
• Schema and query engineering — table and sorting-key design, partitioning, materialised views, and query optimisation against multi-terabyte datasets
• Capacity and observability — a measured capacity model, storage tiering and retention, and Grafana monitoring and alerting
• Ingestion and data quality — correctness and completeness gates on the pipelines feeding the plant; reconciliation that proves nothing was dropped
• Workload isolation — keeping ingest, operations, reporting and ad-hoc analytics from treading on each other as read load grows
• AI-assisted operations — modern tooling (including MCP-based AI access to the estate) that lets a small team run a large platform well

Job description

ClickHouse Engineer

Singapore (Office-based)| Full-Time

About Us

Reactive Markets is the 2026 OTC Trading Platform of the Year (Risk.net). Our network handles over $50 billion in daily trading volumes across FX, Equities, and Cryptocurrency, connecting 40+ of the world’s leading liquidity providers.

We build trading systems that operate at the edge of what’s technically possible — where nanoseconds matter and a dropped message costs real money. Our engineering team is small, senior, and deeply invested in the craft of building cutting edge, reliable, high-performance systems.

The Role

We’re looking for a ClickHouse Engineer to share ownership of our data plant: a multi-datacentre, multi-AZ ClickHouse cluster ingesting billions of rows a day, underpinning compliance, billing, and a growing family of real-time and historical analytics.

This is a core-platform engineering role, not a DBA role. You’ll work across the full engineering scope of a serious ClickHouse estate: cluster operations and upgrades, schema and sorting-key design, materialised views, ingestion performance, query optimisation, workload isolation, capacity planning, storage tiering, backup and recovery, monitoring and alerting, and production support. You’ll also work on the data pipelines around the plant — our capture services are Go, and our analytics foundation is Python.

You don’t need years of ClickHouse specifically — deep experience with another columnar or large-scale time-series database qualifies you, and a strong columnar engineer learns our estate quickly. What we do need is genuine operational depth: you have run a production data platform, owned its capacity plan and its recovery procedures, and stayed calm when it mattered.

This hire completes a follow-the-sun team — based in Singapore (office-based) or London — so the platform is supported across time zones rather than by heroics. On-call is a shared, contracted responsibility, planned and paid — not goodwill.

What You’ll Work On

• Cluster operations at scale — running, upgrading and evolving a multi-AZ ClickHouse estate on Kubernetes, with rehearsed backup and recovery

• Schema and query engineering — table and sorting-key design, partitioning, materialised views, and query optimisation against multi-terabyte datasets

• Capacity and observability — a measured capacity model, storage tiering and retention, and the Grafana monitoring and alerting that keeps the plant’s health visible

• Ingestion and data quality — correctness and completeness gates on the pipelines feeding the plant; reconciliation that proves nothing was dropped

• Workload isolation — keeping ingest, operations, reporting and ad-hoc analytics from treading on each other as read load grows

• AI-assisted operations — modern tooling (including MCP-based AI access to the estate) that lets a small team run a large platform well

What We Need

Technical:

• Columnar/OLAP database engineering — ClickHouse strongly preferred; deep experience with another columnar or large-scale time-series store (BigQuery, Redshift, Druid, kdb+ or similar) also works

• SQL depth — execution plans, query optimisation, and schema design for very large datasets

• Operational/SRE experience — you have carried production responsibility for a data platform: monitoring, capacity, incidents, recovery

• Linux and Kubernetes — comfort with the operational layer beneath the database

• A programming language — one of Python, Go, R, or MATLAB (Python preferred)

• Engineering discipline — you write proposals, strategies, and architectural documents and diagrams well, and you manage change properly

• Git — excellent version-control practice

• Distributed-systems fundamentals — replication, consistency, failure modes

• Financial services experience is a plus but not essential — domain knowledge can be learned; operational instinct cannot

How you work:

• You own production. Calm in an incident, rigorous in a post-mortem, honest about what nearly went wrong

• You measure first. Topology, storage and optimisation decisions follow observed load and cost — not fashion

• You write things down. Runbooks, schema documentation and clean handovers are part of the craft

• You share knowledge deliberately. The goal is a platform supportable by a team, not a specialist

• You communicate across time zones. Follow-the-sun only works with clear, proactive handovers

• You embrace AI as a tool. You use it to amplify your own effectiveness and help shape how it operates safely around production data

What You Get

• A serious estate — genuine scale, genuine criticality: the system of record for a live trading network, not a reporting sidecar

• Real ownership — shared responsibility for a business-critical core platform from day one, working directly with the platform’s lead engineer

• An excellent team — senior engineers who care about craft, collaborate openly, and hold each other to high standards

• Sustainable operations — follow-the-sun coverage by design; on-call is shared, contracted and planned, not heroics

• Competitive package — competitive compensation and benefits aligned to your local market

• Modern tooling — AI-assisted operations and investigation tooling that removes toil rather than adding process

• Growth — the estate is scaling to multiples of today’s volume; the role grows with it

We believe in transparency, honest feedback, and writing things down. We celebrate delivery, not activity. We frame AI as empowering people — removing toil, amplifying capability, enabling higher-value work.

Our Hiring Process

We keep our process focused and respectful of your time. Our process follows three stages.

Stage 1: Initial Conversation with Talent Acquisition
Format: Video call, ~30-45 minutes

Stage 2: Hiring Manager Conversation with the relevant team lead or hiring manager
Format: Video call, ~60 minutes

Stage 3: Technical Interview with two members of the relevant engineering team
Format: Video call, interactive session, ~60 minutes

What to expect in each stage will be explained by Talent Acquisition if you’re invited to interview. Throughout the process, we aim to keep momentum with no unnecessary delays between stages, and feedback typically comes within a few business days.

*Please note that depending on availability, Stage 2 and 3 may swap around.