1. Data Pipeline Engineering Pipeline Development : Architect, build, and optimize complex data pipelines and ETL/ELT workflows to ingest, process, and transform massive data sets from diverse sources.

2. Automation & Transformation : Develop automation scripts and complex data transformations primarily using Python or Groovy.

3. Data Modeling : Design and implement optimized data warehouse models to support high-performance enterprise analytics and reporting.

4. Infrastructure Management : Manage and maintain data infrastructure across AWS cloud environments and existing on-premise setups, ensuring seamless hybrid operations.

5. Data Quality : Ensure the highest standards of data accuracy, performance, and reliability at scale, implementing rigorous testing and validation protocols.

6. Technology & Collaboration : Work on high-impact data infrastructure projects in a fast-paced, collaborative environment.

Required Skills & Technical Expertise :

- 6-10 years of experience in Data Engineering or a closely related quantitative role.

- Strong proficiency in SQL for data manipulation and optimization, and Python for development and automation.

- Hands-on experience with AWS cloud services and proven experience with data warehousing solutions e.g., Redshift, Trino, or advanced PostgreSQL.

Nice to Have Qualifications :

- Familiarity with orchestration tools like Airflow or NiFi.

- Real-Time Processing : Experience with streaming platforms like Kafka, Spark, or Flink.

- Containerization : Working knowledge of Docker and Kubernetes for deployment and container management.

- BI Tools : Experience with analytics platforms such as Athena or Metabase.