Senior Data Engineer

Through proprietary software and AI, along with a focus on customer delight, Sleek makes the back-office easy for micro SMEs.

We give Entrepreneurs time back to focus on what they love doing - growing their business and being with customers. With a surging number of Entrepreneurs globally, we are innovating in a highly lucrative space.

We operate 3 business segments:

1. Corporate Secretary: Automating the company incorporation, secretarial, filing, Nominee Director, mailroom and immigration processes via custom online robots and Sleek Sign.  We are the market leaders in Singapore with ~5% market share of all new business incorporations
2. Accounting & Bookkeeping: Redefining what it means to do Accounting, Bookkeeping, Tax and Payroll thanks to our proprietary Sleek Books ledger, AI tools and exceptional customer service
3. FinTech payments: Overcoming a key challenge for Entrepreneurs by offering digital banking services to new businesses

Sleek launched in 2017 and now has around 15,000 customers across our offices in Singapore, Hong Kong, Australia and the UK.  We have around 450 staff with an intact startup mindset. 

We have achieved >70% compound annual growth in Revenue over the last 5 years and as a result have been recognized by The Financial Times, The Straits Times, Forbes and LinkedIn as one of the fastest growing companies in Asia. Backed by world-class investors, we are on track to be one of the few cash flow positive, tech-enabled unicorns based out of Singapore.

About the Role

We are looking for an experienced Senior Data Engineer to join our growing team. As a key member of our data team, you will design, build, and maintain scalable data pipelines and infrastructure to enable data-driven decision-making across the organization. This role is ideal for a proactive, detail-oriented individual passionate about optimizing and leveraging data for impactful business outcomes.

Key Responsibilities

• Data Pipeline Development: Design, implement, and optimize robust, scalable ETL/ELT pipelines to process large volumes of structured and unstructured data.
• Data Modeling: Develop and maintain conceptual, logical, and physical data models to support analytics and reporting requirements.
• Infrastructure Management: Architect, deploy, and maintain cloud-based data platforms (e.g., AWS, GCP).
• Collaboration: Work closely with data analysts, business owners, and stakeholders to understand data requirements and deliver reliable solutions.
• Data Governance: Ensure data quality, consistency, and security through robust validation and monitoring frameworks.
• Performance Optimization: Monitor, troubleshoot, and optimize the performance of data systems and pipelines.
• Innovation: Stay up to date with the latest industry trends and emerging technologies to continuously improve data engineering practices.