Senior Software Engineer (Data Engineering)

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

Job summary

ScienTec Consulting Pte Ltd is seeking a Senior Software Engineer (Data Engineering) to join a data-focused team within a Singapore government agency. You will own data pipelines end-to-end and influence architectural decisions beyond fixed specs.

Responsibilities include building scalable pipelines for batch and streaming, modeling data, ensuring quality and governance, and collaborating on deployment and AI-enabled features with stakeholders. Hybrid work arrangement is available.

Qualifications

• 5–7 years of hands-on software/data engineering experience.
• Experience building data pipelines for batch and streaming.
• Production backend systems experience.
• Ability to design APIs and architecture.
• Experience with cloud data platforms.

Responsibilities

• Own data pipelines from collection to downstream availability.
• Design data structures to support operational and analytical needs.
• Build tooling to monitor data quality, reliability, governance.
• Contribute to system and product architecture with the team.
• Design for AI-driven or retrieval-based features future-proofing.
• Convey technical trade-offs to the team and stakeholders.
• Collaborate on platform deployment and decisions with engineers and business.
• Handle data responsibly in a regulated public sector environment.

Skills

Data engineering

Data pipelines

API design

System architecture

Cloud platforms

Team leadership

Tools

Databricks

Delta Lake

Unity Catalog

Cloud deployment tooling

Job description

Senior Software Engineer (Data Engineering)

Working Hours: Mon-Fri (Hybrid)

Remuneration: Up to $11,000 + AWS

Employment Type: Contract (1 year renewable with chances of conversion)

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

Required

• 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

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Hazel Hui San Tan - R26163129

ScienTec Consulting Pte Ltd - 11C5781