Responsibilities :

- Develop, maintain, and optimize data pipelines and workflows and Feature Store to ensure seamless data ingestion and transformation as a scalable data solution.

- Design, develop, implement, and architect Data Engineering pipelines, considering performance & scalability including data storage and processing.

- Implement advanced data transformations and quality checks to ensure data accuracy, completeness, security and consistency of data.

- Seamlessly integrate data from diverse sources, for data ingestion, transformation and storage, leveraging AWS S3 Storage and possibly Snowflake as a SQL Data Warehouse.

- Create and implement advanced data models and schemas and ensure data governance and data management best practices.

Qualification and Desired Experiences :

- 7+ years of data analysis and engineering experience

- Bachelors degree in computer science, Statistics, Informatics, Information Systems or another quantitative field.

- Working knowledge of API or Stream-based data extraction processes like Salesforce API and Bulk API and have hands-on experience in web crawling.

Primary Tech skills :

- Advanced Web-crawling & scraping methods and tools

- Building end-end Data Engineering pipelines for Semi and unstructured data (Text, all kinds of simple/complex table structures, images, video and audio data)

- Python, Pyspark, SQL, RDBMS

- Data Transformation (ETL/ELT) activities

- SQL Data warehouse (e.g. Snowflake) working / preferably administration

Secondary Tech skills :

- Databricks

- Familiarity with AWS services : S3, Glue, EMR, EC2, RDS, monitoring and IAM

- Kafka, Spark & Kafka Streaming

- Workflow automation (e.g. using Github actions)

- Performing RCA

Additional Skills :

- Cloud Architectures, Cross Domain Knowledge, Design Thinking, Development Fundamentals, DevOps, Distributed Computing, Microservices Fluency, Full Stack Development, Security-First Mindset, User Experience (UX)