Lead/Senior Data Engineer
1 week ago
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.
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