Data Engineer

Strong hands on experience with Python for data engineering and pipeline development

Proficiency in SQL and experience with large, structured datasets

Exposure to Snowflake or similar cloud data warehouse platforms

Overview

We’re hiring a

Data Engineer

to join a highly structured, enterprise‑grade technology environment undergoing a multi‑year data transformation initiative

for one of our key Financial Services clients

. You’ll work within a modern, collaborative engineering squad responsible for building scalable data ingestion pipelines and enabling high‑quality, centralised data access across critical business domains.

This role is ideal for someone who enjoys

hands‑on engineering

,

data pipeline operations

, and

tackling real‑world production challenges

in a mature environment with strong engineering standards.

What You’ll Do

Design, develop, and maintain robust data ingestion pipelines (batch \& streaming) using Python.

Integrate data from APIs, file transfers, and relational databases (e.g., Oracle, MSSQL) into a cloud‑based data platform.

Build and optimise ETL/ELT processes, ensuring reliability, scalability, and clean end‑to‑end data flow.

Implement automated data quality checks, operational monitoring, and rerun/reprocessing capabilities.

Work across the full SDLC: requirements, development, testing (SIT/UAT), deployment, and BAU support.

Collaborate closely with data stewards, analysts, and cross‑functional stakeholders to deliver high‑quality outcomes.

Participate in operational duties, including occasional low‑touch weekend support for deployment‑related issues.

Uphold strong engineering discipline around compliance, documentation, version control, and CI/CD processes.

What You’ll Bring

Must‑Have Skills

Strong hands‑on experience with Python for data engineering and pipeline development.

Proficiency in SQL and experience with large, structured datasets.

Exposure to Snowflake or similar cloud data warehouse platforms.

Experience with AWS services commonly used in data environments.

Familiarity with CI/CD pipelines (e.g., GitHub).

Strong understanding of data engineering fundamentals: ingestion patterns, orchestration, data lifecycle management.

Ability to troubleshoot production issues independently with an operational mindset.

Strong communication and collaboration skills within structured, cross‑team environments.

Nice‑to‑Have Skills

Test‑driven development (TDD) or production‑grade coding practices.

Experience with pipeline monitoring and observability tools.

Background working in large, mature organisations with established governance frameworks.

Prior exposure to enterprise‑scale data transformation or centralised data platform projects.

Who You Are

You enjoy building and fixing pipelines end‑to‑end, not just writing scripts.

You’re comfortable working in environments where engineering hygiene, traceability, and compliance matter.

You thrive in collaborative, mission‑driven teams with shared ownership.

You’re proactive, structured, and able to navigate complex data ecosystems confidently.

You’re open to light operational duty (very minimal weekend touchpoints during deployment cycles).

Team \& Ways of Working

You’ll join a tight‑knit engineering squad working as one team.

The wider group includes data management partners who support data quality, operations, and user engagement.

Work is delivered in Agile sprints, with strong emphasis on communication and clear ownership.

The culture is inclusive, supportive, and highly collaborative, with team members helping each other across development and operations.

Hybrid work arrangement with rotational in‑office days depending on team schedule.

Why This Role Is Appealing

Be part of a high‑impact, enterprise‑level data initiative that’s central to organisational decision‑making.

Enjoy the stability and structure of a large organisation while working in a modern, engineering‑driven team.

Work on meaningful data domains with real operational importance and visibility.

Opportunity to grow your cloud data engineering skills and gain exposure to end‑to‑end platform operations.

The team invests heavily in mentoring, learning, and continuous improvement.

Tech readiness is complete — you can make immediate impact from day one.

We regret to inform that only shortlisted candidates will be notified.

EA registration number:

ANDREW JONAS MATTHEW,

R21103843

Allegis Group Singapore Pte Ltd, Company Reg No. 200909448N, EA Licence No. 10C4544