Key Responsibilities:
Solution Architecture: Design scalable and secure data pipelines using AWS Glue, PySpark, and related AWS services (EMR, S3, Lambda, etc.)
Leadership & Mentorship: Guide junior engineers, conduct code reviews, and enforce best practices in development and deployment.
ETL Development: Lead the design and implementation of end-to-end ETL processes for structured and semi-structured data.
Framework Building: Develop and evolve data frameworks, reusable components, and automation tools to improve engineering productivity.
Performance Optimization: Optimize large-scale data workflows for performance, cost, and reliability.
Data Governance: Implement data quality, lineage, and governance strategies in compliance with enterprise standards.
Collaboration: Work closely with product, analytics, compliance, and DevOps teams to deliver high-quality solutions aligned with business goals.
CI/CD Automation: Set up and manage continuous integration and deployment pipelines using AWS CodePipeline, Jenkins, or GitLab.
Documentation & Presentations: Prepare technical documentation and present architectural solutions to stakeholders across levels.
Requirements
Required Qualifications:
7–12 years of experience in data engineering or related fields.
Strong expertise in Python programming with a focus on data processing.
Extensive experience with AWS Glue (both Glue Jobs and Glue Studio/Notebooks).
Deep hands-on experience with PySpark for distributed data processing.
Solid AWS knowledge: EMR, S3, Lambda, IAM, Athena, CloudWatch, Redshift, etc.
Proven experience in architecture and managing complex ETL workflows.
Proficiency with Apache Airflow or similar orchestration tools.
Hands-on experience with CI/CD pipelines and DevOps best practices.
Familiarity with data quality, data lineage, and metadata management.
Strong experience working in agile/scrum teams.
Excellent communication and stakeholder engagement skills.
Preferred/Good to Have:
Experience in financial services, capital markets, or compliance systems.
Knowledge of data modeling, data lakes, and data warehouse architecture.
Familiarity with SQL (Athena/Presto/Redshift Spectrum).
Exposure to ML pipeline integration or event-driven architecture is a plus.
Benefits
Flexible work culture and remote options
Opportunity to lead cutting-edge cloud data engineering projects
Skill-building in large-scale, regulated environments.