Senior Backend Engineer (Data Engineering)

SCIENTEC CONSULTING PTE. LTD.; Singapore; Hybrid; SGD 74,000 - 123,000; Part time

Benefits offered by this job

AWS

1 year renewable contract

Job summary

SCIENTEC CONSULTING PTE. LTD. is seeking a Senior Software Engineer (Data Engineering) in Singapore to own data pipelines end‑to‑end and shape data architecture across products.

You will work in a hybrid setup, collaborating with government-related teams on data governance, quality, and platform decisions, while aligning with upcoming AI-ready capabilities.

Qualifications

• 5–7 years of hands-on software engineering with production data systems.
• Strong data engineering: pipelines, data models, batch and streaming data.
• Proficiency in at least one general-purpose language for backend systems.
• Experience across API design, architecture, and full-stack needs.
• Experience with cloud-based data platforms or lakehouse architectures.

Responsibilities

• Own end-to-end data pipelines from ingestion to downstream consumption.
• Design data structures and storage for daily ops and analytics.
• Build tooling to monitor data quality, reliability and governance.
• Contribute to broader system and product architecture with engineers.
• Plan for future flexibility, including AI-driven or retrieval features.
• Advise on technical trade-offs affecting roadmap and decisions.
• Collaborate with stakeholders on deployment and platform choices.
• Handle data responsibly within a regulated public sector context.

Skills

Data engineering

Backend development

API design

Independent work

Stakeholder communication

Tools

Databricks

Delta Lake

Unity Catalog

Lakehouse architectures

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