Data Engineer

Detailed Description From Employer:

description:

• 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