Lead/Senior Data Engineer

About Us

About DATAECONOMY: We are a fast-growing data & analytics company headquartered in Dublin with offices inDublin, OH, Providence, RI, and an advanced technology center in Hyderabad,India. We are clearly differentiated in the data & analytics space via our suite of solutions, accelerators, frameworks, and thought leadership.

Job Description

We are seeking a highly experienced and hands-on Lead/Senior Data Engineer to architect, develop, and optimize data solutions in a cloud-native environment. The ideal candidate will have 7–12 years of strong technical expertise in AWS Glue, PySpark, and Python, along with experience designing robust data pipelines and frameworks for large-scale enterprise systems. Prior exposure to the financial domain or regulated environments is a strong advantage.

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.