Senior Data Engineer (Analytics Engineering)

Secretlab; Singapore; On-site; SGD 140,000 - 200,000; Full time

Job summary

Secretlab is seeking a Senior Data Engineer to own the design, build, and delivery of analytics solutions that drive business decisions. You will focus on Analytics Engineering within the Data Engineering team, translating business problems into scalable data models and serving data across Supply Chain, Finance, Marketing and Operations.

You will lead the architecture and be a technical leader, mentoring others and establishing engineering standards to ensure durable value for the business.

Qualifications

• Strong SQL skills with window functions, CTEs, and query optimization.
• Experience with cloud data warehouses, preferably Snowflake.
• Proficient with Git, branching, PRs, and code review best practices.
• Nice to have exposure to ecommerce data sources such as Shopify, Klaviyo, and Google Analytics.

Responsibilities

• Sprint planning with BI and Analytics teams to refine requirements and priorities.
• Data modeling including star schemas, dimensions, facts, and marts.
• Code reviews with a focus on mentoring and standards.
• Stakeholder collaboration to translate requirements into robust data solutions.
• Maintain and expand the Analytics Engineering Center of Excellence.

Skills

Strong SQL

Tools

Snowflake

Job description

Secretlab is an international gaming chair brand seating over a million users worldwide, with our key markets in the United States, Europe and Singapore, where we are headquartered.

We're looking for a Senior Data Engineer to own the design, build, and delivery of analytics solutions that drive business decisions. Beyond writing SQL, we need someone who understands why the business needs data — someone who can translate business problems into well-architected solutions.

This role sits within the Data Engineering team but specializes in Analytics Engineering — you'll be the technical leader establishing how we model, transform, and serve data to the business. You'll work across Supply Chain, Finance, Marketing, and Operations to translate ambiguous business problems into clean, maintainable data models. You'll partner closely with BI leads, analysts, and business stakeholders to ensure your work creates durable value.

A note on what \"good\" looks like here: We care deeply about modular, scalable design. Code that works today but can’t be easily modified tomorrow creates long-term maintenance burden. If adding a new market or tweaking a calculation requires significant rework, we'd consider that a design gap. We want engineers who build systems that are easy to extend, easy to understand, and easy for others to maintain.

You’ll be expected to own outcomes — not just ship code, but ensure your work solves real business problems. If you’re someone who thrives on building scalable data models, mentoring others, and establishing engineering standards, this role is for you.

To be successful:

Technical Delivery & Design

• Design and own end-to-end analytics architecture — star schemas, dimensions, facts, and marts


• Write modular, loosely-coupled code — changes to one component shouldn’t require rewriting others


• Deliver medium-to-high complexity projects independently with minimal support


• Balance MVP discipline with quality — ship, learn, iterate

Technical Leadership

• Drive the Analytics Engineering Center of Excellence — establish modeling standards, testing patterns, and documentation practices


• Mentor and coach junior engineers through code reviews and active skill development


• Develop reusable patterns and macros that elevate the whole team’s output

Communication & Stakeholder Partnership

• Partner with BI leads, analysts, and business stakeholders to translate requirements into solutions


• Own the \"what\" and \"why\" behind data models — not just the \"how\"


• Escalate blockers early — we value communication over solo problem-solving

What will your week look like?

• Sprint planning with BI and Analytics team to refine requirements and prioritize work


• Data modeling — designing and building star schemas, event-based marts, and feature tables


• Code reviews — both giving and receiving, with a focus on mentoring and standards


• Stakeholder collaboration — partnering with business teams to validate requirements and outputs


• Center of Excellence — maintaining modeling standards, updating templates, coaching sessions

Requirements:

Technical — Must Have

• Strong SQL — window functions, CTEs, query optimization


• Cloud data warehouse — Snowflake strongly preferred


• Git — branching strategies, PRs, code review best practices

Technical — Nice to Have

• Exposure to eCommerce data sources (Shopify, Klaviyo, Google Analytics)

Personality

• Ownership mindset — accountable for outcomes, not just outputs


• Clear communicator — distills complexity for non-technical stakeholders


• Honest and pragmatic — admits when they don’t know something


• Teacher mentality — enjoys helping others grow