Job Summary :

We are seeking a highly experienced Senior Cloud Data Engineer with 10+ years of hands-on experience in designing, building, and managing large-scale data platforms and pipelines in cloud environments.

The ideal candidate will have strong expertise in AWS, Snowflake, PySpark, and ETL frameworks, along with proven leadership experience in delivering scalable, secure, and high-performance data solutions.

This role requires close collaboration with business stakeholders, cross-functional teams, and mentoring junior engineers while driving data-driven decision-making.

Key Responsibilities :

- Design, develop, and maintain scalable cloud-based data pipelines using AWS services such as S3, EMR, Glue, Lambda, Kinesis, and Snowflake.

- Lead end-to-end data ingestion, transformation, and orchestration workflows using PySpark, Apache Airflow, Kafka, and ETL tools.

- Architect and optimize data warehousing solutions on Snowflake, Hive, Redshift, and HDFS for large-volume and high-performance analytics.

- Manage and execute on-prem to cloud data migrations, ensuring minimal downtime, cost optimization, and improved scalability.

- Implement and enforce data governance, security, audit logging, and compliance standards across data platforms.

- Develop and optimize PySpark jobs for data ingestion into Snowflake, Hive, and HBase tables.

- Monitor and tune system performance, including query optimization, error handling, and recovery mechanisms.

- Collaborate with BI and analytics teams to support reporting solutions using Power BI and Tableau.

- Lead and mentor a team of data engineers, conducting code reviews and promoting best practices.

- Work in an Agile/Scrum environment, participating in sprint planning, POCs, and continuous improvement initiatives.

- Coordinate with cross-functional teams and stakeholders to deliver business-aligned data solutions.

Required Skills & Experience :

- 1012+ years of experience in Data Engineering with strong exposure to cloud-based data platforms.

- Strong hands-on experience with AWS (EMR, Glue, Lambda, S3, Kinesis, ECS).

- Expertise in Snowflake (development and administration).

- Advanced proficiency in Python and PySpark, including data structures and distributed processing.

- Solid experience with ETL tools such as Informatica PowerCenter, Informatica BDM/BDE, Alteryx, and DBT.

- Strong knowledge of Big Data technologies: Hadoop, Hive, HDFS, HBase, Spark.

- Experience with real-time data processing using Kafka, ActiveMQ, and Spark Streaming.

- Proficiency in SQL and databases: Oracle, Hive, Snowflake, Redshift, Netezza, Sybase.

- Hands-on experience with job scheduling tools: Airflow, Control-M, Autosys, Tidal, Cron.

- Experience in performance optimization, data validation, audit logging, and error handling.

- Exposure to Banking, Investment, or Financial Services domains is a strong plus.

Preferred Qualifications :

- AWS Certified Solutions Architect Associate.

- AWS Certified Developer Associate.

- Experience working with BI tools such as Power BI and Tableau.

- Exposure to Databricks and modern analytics platforms.

- Strong stakeholder management and leadership skills.

Ideal Candidate Profile :

The ideal candidate is a hands-on technical leader who can balance architecture, development, and team leadership.

You should be comfortable working in fast-paced environments, handling complex data challenges, and delivering reliable, business-ready data solutions at scale.