We are seeking a highly skilled Data Engineer with experience in cloud-based data platforms to build scalable, reliable data pipelines and robust data models. This role will work closely with data teams, AI teams, and business stakeholders to ensure a solid data foundation that supports analytics, reporting, machine learning, and downstream data products.
Job Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines, including ingestion, cleaning, transformation, and loading into data lakes and data warehouses.
- Collaborate with Data Science, BI, Product, and Backend teams to translate business and analytical needs into reliable data models and table structures.
- Build and optimize Bronze, Silver, and Gold layers to ensure data consistency, performance, and usability.
- Manage batch and streaming data processing frameworks such as Spark, Flink, or Kafka, ensuring system stability and efficiency.
- Implement and maintain data quality monitoring, including schema validation, row-count checks, anomaly detection, and pipeline automation.
- Provide foundational datasets and feature pipelines to support AI and analytics teams.
- Work with platform and infrastructure teams to ensure availability, security, and scalability of the data platform.
- Contribute to data governance practices, including metadata management, data cataloging, field definitions, and versioning standards.
- Continuously improve pipeline performance, reduce processing costs, and enhance maintainability.
Qualifications
- 35 years of experience in data engineering or backend engineering, with hands-on experience in large-scale data processing.
- Bachelor's degree or above in Computer Science, Information Systems, Data Engineering, or related fields.
- Strong proficiency in SQL and experience with Python or Scala for data processing.
- Experience with at least one major cloud provider (AWS / GCP / Azure); familiarity with S3, Glue, Lambda, Databricks, or similar platforms.
- Knowledge of distributed data processing technologies such as Spark, Flink, or Kafka.
- Solid understanding of data warehousing concepts and data modeling (Star Schema, Data Vault, Medallion Architecture).
- Experience with ETL/ELT pipeline orchestration tools such as Airflow, dbt, or Dagster.
- Strong communication skills and ability to collaborate with cross-functional stakeholders.
- Detail-oriented, proactive, and strong problem-solving mindset.
- Experience in fintech, trading platforms, or risk-related data is a strong advantage.
What we offer
- Clear role definition with well-defined objectives
- Extensive cross-functional and cross-regional collaboration opportunities
- Diverse data scenarios with challenging product strategy initiatives
- Fast-paced and dynamic industry environment
- Strong sense of ownership
- Competitive compensation package within a performance-driven culture
