
Lead Data Engineer
- Singapore
- Permanent
- Full-time
- Collaborate with teams of experienced actuaries, data analysts, architects, data engineers and business experts to deliver projects.
- Design and implement scalable data architectures that align with business objectives, including data models, meta data management, and database schemas.
- Define standards, frameworks, and best practices for Spark-based ETL/ELT Development.
- Drive automation and infrastructure-as-code practices (e.g., Terraform, Databricks CLI/API).
- Perform data analysis to troubleshoot and resolve data-related issues.
- Implement data warehouse and lake house solutions to support data and analytics initiatives.
- Assist in the support and enhancements of existing data pipelines and databases and troubleshoot performance issues.
- Participate in database design and modelling.
- Provide technical leadership and guidance to the data engineering team in implementing architectural designs.
- Work closely with cross-functional teams to understand business requirements and translate them into scalable and efficient data solutions.
- Stay current with industry trends and emerging technologies to drive continuous improvement in data architecture strategies and best practices.
- 10+ years of total experience working with data integration teams.
- 5+ years of experience in data architecture, database design, and data management
- 4+ years of in-depth experience developing data pipelines within an Apache Spark environment (preferably Databricks).
- 3+ years of active hands-on work with Databricks, demonstrating in-depth experience.
- Strong hands-on knowledge on Pyspark, Python and SQL and distributed computing principles.
- Experience in tuning spark jobs for performance and cost, addressing complex issues such as data skew, shuffle inefficiencies, and memory leaks, etc.
- Strong knowledge of data modelling, database technologies, data warehousing.
- Experience with ETL/ELT processes and tools.
- Knowledge of cloud platforms (Aws or Azure) and big data technologies (Hadoop, Spark, etc.)
- Understanding of data governance, security, and privacy frameworks.
- Experience with BI and analytics tools like Power BI or Tableau.
- Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
- Experience integrating AI into data engineering.
- Certifications in Databricks, cloud computing or database management.
- Experience developing data solutions using native IaaS and PaaS solutions on AWS (RDS, S3).
- Experience in any object-oriented programming language.