Data Engineering Manager – Big Data | Unison Consulting | Singapore

Position Summary

Unison Consulting is actively seeking an experienced, high-caliber, and technical Data Engineering Manager specializing in Big Data with 10+ years of progressive professional experience in data engineering, enterprise data platforms, and team leadership to join our client operations on a full-time permanent on-site basis in Singapore (specifically targeting banking and financial services domains). In this critical data leadership and architecture role, you will be responsible for guiding technical teams, troubleshooting complex Big Data engineering challenges, and overseeing large-scale data processing solutions. You will leverage your deep specialist expertise in Python, PySpark, SQL, and distributed data processing frameworks to deliver uncompromised data reliability, high architectural scalability, and operational excellence. This opportunity offers competitive compensation ($8,000 – $10,000), executive technical visibility, and exceptional career growth prospects within a premier global technology consulting and financial services enterprise.

Detailed Job Description

As a Data Engineering Manager supporting Unison Consulting in Singapore, you will take full strategic, tactical, and technical ownership of enterprise data architecture, Big Data platform development, ETL/ELT pipeline optimization, and cross-functional team leadership across mission-critical financial systems. Your day-to-day responsibilities include designing, developing, and managing complex data pipelines, large-scale ETL/ELT workflows, and distributed data processing solutions, providing hands‑on technical guidance and troubleshooting support for engineering teams tackling complex Big Data challenges, establishing robust data modeling and data architecture standards to ensure high platform scalability and performance optimization, collaborating closely with cross-functional software engineering, risk analytics, regulatory reporting, and executive stakeholder squads in Agile sprint cycles, conducting rigorous code reviews, architecture evaluations, and performance tuning across enterprise datasets, preparing comprehensive technical documentation of data pipelines, platform blueprints, and operational runbooks.

Key Responsibilities

• Lead enterprise-level data engineering initiatives, Big Data platform development, and technical team execution.
• Provide hands‑on technical guidance and expert troubleshooting for complex Big Data architecture and distributed processing challenges.
• Design, build, optimize, and maintain complex data pipelines, ETL/ELT workflows, and large-scale data processing solutions.
• Establish robust data modeling, data architecture standards, performance optimization, and platform scalability protocols.
• Manage data engineering team deliverables, sprint planning, technical execution, and professional mentoring.
• Collaborate seamlessly with cross‑functional banking, financial services, risk analytics, and regulatory reporting squads.
• Drive the adoption of cloud‑based data engineering practices, modern data platforms, and automated deployment pipelines.
• Maintain accurate, comprehensive technical documentation of data models, pipeline topologies, and operational runbooks.

Required Qualifications & Skills

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related technical discipline.
• Minimum of 10+ years of progressive professional experience in data engineering, Big Data development, and enterprise data platforms.
• Strong, demonstrable hands‑on professional expertise in Big Data technologies, Python, PySpark, SQL, and distributed data processing frameworks.
• Comprehensive working knowledge and practical proficiency in building and managing complex data pipelines and ETL/ELT workflows.
• Deep practical experience leading data engineering teams, managing deliverables, and driving technical execution.
• Solid practical experience in data modeling, data architecture, performance optimization, and data platform scalability.
• Exceptional analytical, troubleshooting, debugging, and leadership capabilities under high‑pressure conditions.
• Excellent written and verbal English communication skills with strong executive stakeholder presentation abilities.
• Mandatory legal work eligibility and physical availability for full‑time professional operations in Singapore (immediate to 30 days notice).

Nice-to-Have Skills

• Professional certifications such as Databricks Certified Professional Data Engineer, AWS Certified Data Analytics, or TOGAF Enterprise Architecture.
• Prior professional working experience in top‑tier banking, financial services, regulatory reporting, or risk analytics environments.
• Hands‑on familiarity with cloud‑native data platforms (AWS, Azure, GCP) and modern orchestration tools (Airflow, Prefect).
• Experience utilizing CI/CD deployment pipelines, GitHub Actions, and containerization platforms (Docker, Kubernetes).
• Exposure to agile software development methodologies and enterprise IT service management (ITSM) ticketing platforms (ServiceNow, Jira).