Senior Full-Stack Data Engineer

Job Overview

We are seeking an experienced Senior Full-Stack Data Engineer with 10–15 years of hands-on experience in data engineering, data architecture, and cloud-native data platforms. The ideal candidate will have strong expertise in AWS, SQL, Python, PySpark, ETL/ELT, and modern data technologies.

This role will be responsible for designing, developing, optimizing, and supporting next-generation data platforms at scale. The candidate will also provide technical leadership, contribute to architectural decisions, mentor team members, and deliver high-impact data solutions in a fast-paced environment.

Key Responsibilities

• Design and develop scalable data pipelines, ETL/ELT frameworks, and cloud-based data platforms.
• Architect and optimize data solutions using AWS services and modern data engineering technologies.
• Build and maintain high-volume, petabyte-scale data infrastructure in cloud-native environments.
• Develop robust data processing solutions using Python, PySpark, and SQL.
• Work with data lake and lakehouse technologies, including Apache Iceberg and AWS Glue Catalog.
• Develop and optimize data models and transformations using dbt Core.
• Work with data warehouses and query engines such as Snowflake, Redshift, and Athena.
• Implement and support data integration solutions using Talend and AWS services.
• Leverage AWS streaming technologies for near-real-time data processing.
• Implement appropriate security, access controls, networking, and governance using AWS IAM, VPC, S3, and related services.
• Use Git and modern development practices for source control, collaboration, and CI/CD.
• Monitor and troubleshoot data platforms and pipelines using tools such as Splunk.
• Collaborate with architects, developers, analysts, and business stakeholders to deliver reliable data solutions.
• Provide technical leadership, code reviews, mentoring, and guidance to other data engineers.

Required Skills & Experience

• 10–15 years of progressive experience in Data Engineering, Data Architecture, or Full-Stack Data Engineering.
• Strong understanding of data engineering principles, ETL/ELT patterns, data modeling, metadata management, and data governance.
• Extensive hands-on experience with AWS, particularly S3, IAM, VPC, Glue, Athena, and related services.
• Strong programming skills in Python and PySpark.
• Advanced SQL skills with experience in performance tuning and complex data transformations.
• Experience with Talend, dbt Core, Apache Iceberg, and AWS Glue Catalog.
• Hands-on experience with Snowflake, Amazon Redshift, and Amazon Athena.
• Experience with AWS streaming services and large-scale data processing.
• Strong understanding of Git, CI/CD, and software engineering best practices.
• Experience designing and managing large-scale or petabyte-scale data platforms.
• Strong problem-solving, communication, and technical leadership skills.

Preferred Qualifications

• Experience in the insurance industry or other data-intensive enterprise environments.
• Experience with cloud-native data lake/lakehouse architecture.
• Knowledge of data security, governance, lineage, and metadata management.
• Experience mentoring engineers and driving technical architecture decisions.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field is preferred.