Senior Database Engineer

Key Responsibilities

• Design and implement scalable data platforms leveraging Data Lake, Lakehouse, and Data Mesh architectures
• Build and optimize data pipelines for batch and real-time processing using tools like Databricks, Spark, DBT, and cloud-native services·
• Develop robust data ingestion frameworks for structured, semi-structured, and unstructured data (APIs, files, streaming sources)
• Design and develop scalable Python-based micro services to enable secure and efficient data sharing across systems and applications
• Build RESTful APIs and/or event-driven services for exposing curated datasets from Data Lake/Lakehouse platforms
• Work extensively with Python, PySpark, and SQL for data transformation and processing
• Implement streaming pipelines using Kafka / Kinesis and integrate with downstream analytics systems
• Design and manage large-scale datasets using formats such as Parquet, JSON, CSV, and sensor/IoT data
• Optimize data storage, partitioning, and query performance for high-volume analytical workloads· Collaborate with cross-functional teams (Data Architects, Analysts, BI teams) to operationalize data lake solutions
• Contribute to data modeling in Lakehouse environments (medallion architecture, dimensional modeling)
• Ensure data quality, reliability, and observability across pipelines
• Implement serverless data processing solutions where applicable

Required Skills & Experience

• 4–6 years of experience in Data Engineering or related roles
• Strong expertise in Python, PySpark, and SQL
• Hands-on experience with Databricks, Delta Lake, or Snowflake
• Experience with ETL / ELT frameworks and orchestration tools
• Practical exposure to streaming technologies like Kafka or Kinesis
• Strong understanding of batch processing frameworks such as Spark, AWS Glue, or DBT
• Proficiency in handling structured, semi-structured, and unstructured data
• Experience with modern data modeling techniques (Star Schema, Snowflake Schema, 3NF)
• Good understanding of Data Lakehouse concepts and implementation patterns
• Familiarity with serverless architectures (e.g., Python-based serverless pipelines)