Data Platform Engineer

Responsibilities:

• Build and maintain scalable ETL/ELT data pipelines for structured and unstructured data.
• Design and optimise batch and real-time streaming pipelines using Spark, Kafka, Flink, etc.
• Develop and maintain data models for analytics and product use cases.
• Write clean, scalable, production-grade code using Python, Java, Go, or Ruby.
• Build internal data services, integrations, automation, and developer tools.
• Ensure data quality, integrity, availability, reliability, and performance.
• Implement monitoring, logging, alerting, and automated testing for data pipelines.
• Troubleshoot pipeline failures and optimize performance, scalability, and cost.
• Collaborate with Product, Analytics, and Engineering teams on data requirements.
• Participate in system design and architecture discussions.
• Follow best practices around data governance, security, and compliance.

Requirements:

• 3-8 years of experience in data platform or data engineering roles.
• Strong proficiency in SQL and experience with relational and NoSQL databases.
• Hands-on experience with Airflow, DBT, Spark, or Flink.
• Experience with streaming or batch data processing systems.
• Proficiency in at least one programming language (Python, Go, or Java).
• Familiarity with cloud platforms (AWS, GCP, or Azure).

Nice to Have:

• Experience with real-time streaming (Kafka, Kinesis, Pub/Sub).
• Exposure to data warehouses (Snowflake, BigQuery, Redshift).
• Experience operating data platforms in high-scale or fintech domains.
• Understanding of data security, privacy, and compliance.