Data Engineering Manager - Big Data

Unison Group; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

Unison Group in Singapore is seeking a technically strong Data Engineering Manager to lead and mentor a team responsible for designing, developing, and delivering scalable enterprise data platforms and pipelines. You will shape data architecture, guide engineers, and ensure production-ready code across large-scale data workloads.

The ideal candidate has 10+ years of data engineering and hands-on Big Data expertise, with proficiency in Python, PySpark, SQL, and cloud-based data platforms

Qualifications

• 10+ years of experience in data engineering and enterprise data platforms.
• Hands-on expertise with Big Data tech and scalable architectures.
• Strong Python, PySpark, and SQL proficiency.
• Experience leading data engineering teams and projects.

Responsibilities

• Lead and mentor data engineering teams to deliver scalable data platforms.
• Oversee ETL/ELT pipelines, data ingestion, and data transformation.
• Drive data architecture, performance tuning, and large-scale processing.
• Ensure data lake and data warehouse initiatives align with governance and security.
• Collaborate with architects and stakeholders to translate requirements into solutions.

Skills

Python

SQL

Team leadership

Data modeling

Data governance

Tools

Hadoop

Spark

PySpark

Hive

Kafka

Databricks

Job description

Location: Singapore
Experience: 10+ years in Data Engineering, including hands‑on Big Data expertise and team management
Domain: Banking / Financial Services preferred

Key Responsibilities

• Lead and manage data engineering teams to design, develop, and deliver scalable enterprise data platforms and pipelines.
• Maintain strong hands‑on expertise in Big Data technologies such as Hadoop, Spark, PySpark, Hive, Kafka, and distributed data processing.
• Design and implement robust ETL/ELT pipelines, data ingestion frameworks, data transformation, and data integration solutions.
• Drive data engineering architecture, technical design, performance tuning, and optimization of large‑scale data processing workloads.
• Manage data lake, data warehouse, and modern cloud‑based data platform implementations.
• Work with cloud platforms such as AWS, Azure, or GCP and technologies such as Databricks, where applicable.
• Lead data quality, data governance, metadata management, security, and data reliability initiatives.
• Collaborate with solution architects, data architects, business stakeholders, and project managers to translate business requirements into technical solutions.
• Manage project delivery, resource allocation, technical reviews, risk mitigation, and delivery timelines.
• Mentor engineers, establish engineering best practices, and ensure high‑quality, maintainable, and production‑ready code.

Required Skills and Experience

• 10+ years of experience in data engineering, Big Data development, and enterprise data platforms.
• Proven hands‑on experience in Big Data technologies, with the ability to guide teams technically and troubleshoot complex engineering challenges.
• Strong proficiency in Python, PySpark, SQL, and distributed data processing frameworks.
• Experience building and managing complex data pipelines, ETL/ELT workflows, and large‑scale data processing solutions.
• Demonstrated experience leading data engineering teams, managing deliverables, and driving technical execution.
• Strong understanding of data modelling, data architecture, performance optimization, and data platform scalability.
• Experience with cloud‑based data engineering and modern data platforms is highly desirable.
• Excellent stakeholder management, communication, problem‑solving, and leadership skills.

Preferred: Experience in banking, financial services, regulatory reporting, risk analytics, or other data‑intensive enterprise environments.

Ideal Candidate: A technically strong Data Engineering Manager who combines practical Big Data development experience with proven people leadership, solution delivery, and the ability to guide engineers through complex data engineering challenges.