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, andSQL

-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, orDBT

-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)