Senior Software Engineer (Data Engineering)

ScienTec Consulting Pte. Ltd.; Singapore; Hybrid; SGD 74,000 - 123,000; Full time

Job summary

ScienTec Consulting Pte. Ltd. in Singapore is seeking a Senior Software Engineer (Data Engineering) to own end-to-end data pipelines for a government-focused data platform. You will design data models, build scalable backends, and collaborate with stakeholders on architecture and governance in a hybrid setup.

The role emphasizes hands-on leadership, independence, and delivering robust data solutions while shaping future AI-driven capabilities in a regulated public-sector environment.

Qualifications

• Minimum 5–7 years of hands-on software engineering with data systems.
• Experience building end-to-end data pipelines.
• Ability to design scalable data models and APIs.
• Familiarity with lakehouse technologies and cloud deployments.

Responsibilities

• Own data pipelines end-to-end, from collection to downstream availability.
• Design data structures and storage for operational and analytical use.
• Build tooling to monitor data quality, reliability, governance.
• Contribute to wider system and product architecture with engineers.
• Prepare for AI-driven or retrieval-based features with future-proofing.
• Participate in key technical decisions and trade-offs.
• Collaborate with stakeholders on platform and deployment choices.
• Handle data responsibly in a regulated public sector environment.

Skills

Data pipelines

Production backends

API design

System architecture

Cloud platforms

Lakehouse tech

Technical leadership

Tools

Databricks

Delta Lake

Unity Catalog

Job description

• Working Hours: Mon-Fri (Hybrid)
• Remuneration: Up to $11,000 + AWS
• Employment Type: Contract (1 year renewable with chances of conversion)

Senior Software Engineer (Data Engineering)

JOB OVERVIEW

We are seeking an experienced Full Stack Engineer to join a data-focused engineering team within a Singapore government agency. This role owns the systems that collect, structure, and serve data across the organisation's products, while also contributing to broader application architecture as priorities evolve. It's a hands-on, highly autonomous role suited to someone comfortable leading technical direction rather than just executing a fixed spec.

RESPONSIBILITIES

• Take ownership of data pipelines from end to end — how data is collected, processed, and made available downstream
• Design data structures and storage approaches that serve both day-to-day operational needs and analytical use cases
• Build tooling and processes to monitor data quality, reliability, and governance
• Contribute to wider system and product architecture alongside other engineers as team priorities shift
• Build with future flexibility in mind, including systems that could support AI-driven or retrieval-based features
• Weigh in on key technical decisions, raising trade-offs that affect the broader team or roadmap
• Work with engineering and business stakeholders on platform and deployment choices
• Handle data responsibly given the sensitivity and constraints of a regulated public sector environment

REQUIREMENTS

• At least 5–7 years of hands‑on software engineering experience, with a track record of owning production data systems from start to finish
• Solid grounding in data engineering practices — building pipelines, structuring data models, and handling both scheduled (batch) and continuous (streaming) data processing
• Strong command of at least one general‑purpose programming language, with experience shipping production backend systems (not limited to data scripts)
• Well‑rounded software engineering skills, including API design, system architecture, and the ability to work across the stack as needed
• Experience with cloud‑based data platforms or "lakehouse"-style architectures
• Comfortable working independently and taking the lead on technical decisions
• Able to clearly communicate technical trade‑offs to non‑technical stakeholders

Good to have

• Familiarity with tools such as Databricks, Delta Lake, Unity Catalog, or similar lakehouse technologies
• Experience building data pipelines that support AI/ML use cases — e.g. retrieval‑augmented generation, embeddings, or vector databases
• Experience with cloud‑native deployment tooling
• Relevant certifications, and/or scores from technical assessments, where available