Who are we
dtcpay is a MAS licensed payment service provider that bridges traditional finance and digital assets. We enable businesses to accept and make payments in both fiat and digital currencies, delivering secure, efficient, and seamless payment experiences across borders. As we expand globally, we are shaping the future of digital payments.
We are also recognised as one of Singapore's Top 10 Startups in the LinkedIn Top Startups 2025 list, a reflection of our momentum and the exciting journey ahead for our team.
We are looking for an experienced Senior Data Engineer to establish and scale our company's data platform from the ground up. As our first dedicated Data Engineer, you will be responsible for designing and implementing a modern data architecture, building scalable data pipelines, and enabling business intelligence across the organization. You will work closely with different stakeholders to transform raw data into reliable, actionable insights that support business growth and strategic decision-making. This role is ideal for someone who enjoys building data infrastructure from scratch and has hands-on experience with modern cloud data platforms.
What You'll Do:
- Design, develop, and maintain scalable, secure, and high-performance data pipelines to collect, process, and transform data from multiple internal and external sources.
- Design and implement a modern cloud-based data platform using Snowflake, Databricks, or equivalent technologies.
- Build and maintain data warehouses, data lakes, or lakehouse architectures to support analytics and reporting.
- Develop ETL/ELT workflows to ensure data is accurate, consistent, and readily available.
- Collaborate with Software Engineers to integrate application data into a centralized data platform.
- Design efficient data models for reporting, dashboards, operational analytics, and business intelligence.
- Partner with Product and business stakeholders to understand reporting and analytics requirements and translate them into scalable technical solutions.
- Establish best practices for data governance, data quality, metadata management, documentation, and security.
- Optimize database performance, storage, query efficiency, and overall platform scalability.
- Monitor data pipelines and troubleshoot production issues to ensure high availability and reliability.
- Support Business Intelligence tools and enable self-service analytics across departments.
- Evaluate and implement modern data engineering technologies, frameworks, and industry best practices.
What We're Looking For:
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related discipline.
- Minimum 5 years of experience in Data Engineering, Data Platform Engineering, or similar roles.
- Hands-on experience with Snowflake or Databricks is required.
- Strong proficiency in SQL with solid knowledge of relational databases and dimensional data modeling.
- Strong programming skills in Python, Java, or Scala.
- Experience designing and developing ETL/ELT pipelines.
- Experience with distributed data processing frameworks such as Apache Spark.
- Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Familiarity with workflow orchestration tools such as Apache Airflow, dbt, or similar.
- Experience handling structured and semi-structured data formats (JSON, Parquet, Avro, etc.).
- Strong understanding of data governance, data quality, security, and access control.
- Experience supporting Business Intelligence tools such as Power BI, Tableau, Looker, or similar.
- Excellent analytical, problem-solving, and communication skills.
- Self-motivated with the ability to work independently in a fast-paced environment.
- Proficiency in both English and Mandarin as you will need to work closely with Chinese vendors.
- The role is based fully onsite, requiring your presence in the office.
Nice to Have:
- Experience building enterprise-scale data warehouse, data lake, or lakehouse solutions.
- Experience with streaming platforms such as Kafka, Kinesis, or Pub/Sub.
- Experience in fintech, payments, banking, blockchain, or financial services industries.
- Knowledge of CI/CD pipelines, Infrastructure as Code, and DevOps practices.
- Familiarity with machine learning data pipelines is an advantage.
- Experience supporting regulatory reporting, compliance, fraud detection, or operational analytics is a plus.
