Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessLog in to see why each signal reads the way it does.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and maintain scalable batch and near-real-time data pipelines using technologies like Apache Kafka, Apache Flink, Python, and SQL.
Support data warehouse and lakehouse platforms including data ingestion, transformation, schema evolution, and performance optimization using technologies such as Apache Iceberg, Apache Spark, Trino, and StarRocks.
Ensure data pipeline quality, monitoring, troubleshooting, and collaborate with cross-functional teams to deliver reliable data solutions for analytics and business use cases.
Minimum Requirements
2–4 years of professional experience in Data Engineering, Software Engineering, Analytics Engineering, or related discipline.
Proficiency in Python and SQL programming, with strong understanding of data structures, algorithms, relational databases, data modeling, and large-scale data architectures.
Experience building and supporting ETL/ELT data pipelines as well as working with data warehouses, data lakes, or lakehouse platforms.
Hands-on experience with cloud platforms, preferably AWS (Amazon S3, EMR, Redshift, MSK) and practical knowledge of technologies such as Apache Spark, Apache Kafka, Apache Flink, Apache Iceberg, Trino, and StarRocks.
Ideal Candidate Profile
Experienced in developing and optimizing both batch and near-real-time data pipelines for large-scale data environments.
Comfortable working in agile and collaborative engineering teams focusing on scalable data solutions and operational reliability.
Background or interest in fintech, payments, or high-volume transactional systems, with familiarity in modern cloud data engineering technologies and best practices.