Data Engineer

• Design and build micro data lakes tailored to lending domain.
• Define and implement enterprise data strategies including modelling, lineage, and governance.
• Architect and build robust data pipelines for batch and real-time data ingestion.
• Develop strategies for extracting, transforming, and storing data from APIs, PDFs, logs, databases, and more.
• Establish best practices for data quality, metadata management, and data lifecycle control.
• Hands-on in implementation of processes, strategies and tools to create differentiated products. – MUST HAVE.
• Collaborate with engineering and product teams to align data architecture with business goals.
• Evaluate and integrate modern data platforms and tools such as Databricks, Spark, Kafka, Snowflake, AWS, GCP, and Azure.
• Mentor data engineers and advocate for engineering excellence in data practices.

Skill & Qualification:

• 4+ years of experience in data engineering.
• Deep understanding of structured and unstructured data ecosystems.
• Hands-on experience with ETL, ELT, stream processing, querying, and data modelling.
• Proficiency in tools and languages such as Spark, Kafka, Airflow, SQL, Amundsen, Glue CatLog, and Python.
• Expertise in cloud-native data platforms including AWS, Azure, or GCP.
• Strong grounding in data governance, privacy, and compliance standards.
  • A strategic mindset with the ability to execute hands-on when needed.