Data Engineer

This role is for one of the Weekday's clients

Data Engineer

10-20 years of industry experience

AWS-based data ecosystems

lakehouse architectures

Python and SQL

Requirements

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and data platforms using AWS services such as S3, Glue, EMR, Redshift, Athena, Lambda, and related ecosystem tools.
• Architect and implement lakehouse solutions, enabling unified data storage for structured and unstructured data with support for analytics and machine learning workloads.
• Build robust ETL/ELT pipelines using Python and SQL, ensuring high data quality, accuracy, and performance.
• Optimize data models, queries, and storage formats to support large-scale analytical workloads and low-latency access.
• Collaborate with analytics, data science, and business teams to translate data requirements into scalable technical solutions.
• Implement data governance, security, and compliance best practices, including access controls, encryption, and monitoring.
• Lead performance tuning, cost optimization, and reliability improvements across AWS data infrastructure.
• Provide technical leadership and mentorship to junior and mid-level data engineers, setting standards for code quality, testing, and documentation.
• Drive adoption of modern data engineering practices, including automation, observability, and CI/CD for data workflows.
• Participate in architectural reviews and contribute to long-term data platform strategy and roadmap.
  

Required Skills & Experience

• 10-20 years of experience in data engineering, data platform development, or large-scale data systems.
• Strong hands-on expertise with AWS data services and cloud-native data architectures.
• Proven experience designing and implementing lakehouse architectures.
• Advanced proficiency in Python for data processing, orchestration, and automation.
• Expert-level SQL skills, including query optimization and complex analytical queries.
• Solid understanding of data modeling, schema design, and data warehousing concepts.
• Experience working with large datasets, distributed systems, and high-volume data pipelines.
• Strong problem-solving skills with the ability to work independently on complex technical challenges.
  

Nice to Have

• Experience with streaming or near-real-time data pipelines.
• Exposure to data governance, metadata management, or data quality frameworks.
• Prior experience in mentoring teams or leading large-scale data initiatives.