Lead Data Engineer

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The ALDO Group is seeking a Lead Data Engineer to join our Data & Analytics team. This role is responsible for leading the design, development, and operation of scalable data solutions that support analytics, reporting, and existing data driven and AI enabled applications across the organization.

As a senior technical leader, the Lead Data Engineer will play a key role in shaping data engineering and analytics engineering practices, designing and evolving data models and architectures, and ensuring data platforms are reliable, scalable, and trusted. The role requires deep hands-on expertise in data engineering, strong architectural thinking, and the ability to collaborate closely with cross functional teams to deliver high quality data products.

The ideal candidate brings extensive experience building production grade data pipelines on AWS, a strong foundation in data modeling and analytics engineering, and practical experience supporting advanced analytics or AI enabled use cases from a data engineering and operational perspective. This role is not focused on building machine learning models, but on enabling, operating, and improving the data foundations that power analytics and AI driven applications.

Responsibilities

• Lead the design, development, and operation of data solutions and ETL/ELT pipelines that power analytics and existing data driven applications, including AI enabled use cases already in production
• Provide technical leadership across data engineering and analytics engineering through solution design, code reviews, best practice definition, and mentorship
• Partner with cross functional teams including analytics, product, and application teams to gather requirements, define data contracts, and deliver reliable datasets and data products
• Design and maintain scalable data models for analytics and operational consumption, ensuring consistency across raw, curated, and consumption layers
• Leverage AWS services and tooling including AWS Data Lake, Redshift, dbt, AWS Glue, Apache Hudi, Apache Athena, AWS Lambda, and SageMaker where applicable to build and deploy robust data workflows
• Improve pipeline performance, reliability, scalability, and cost efficiency through monitoring, optimization, and automation
• Implement and maintain data quality checks, validation, and observability to ensure trusted data for reporting and AI enabled applications
• Troubleshoot and resolve data processing, data quality, and production issues, including root cause analysis and permanent fixes
• Maintain clear documentation for pipelines, data models, and operational runbooks to support maintainability and onboarding
• Stay current with industry best practices in data engineering, analytics engineering, and data enablement for advanced analytics use cases

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related field
• 8 or more years of hands-on experience in data engineering and analytics engineering, with a strong focus on building and operating production-grade data pipelines
• Proven experience designing, building, and maintaining data solutions using AWS technologies including AWS Data Lake, Redshift, dbt, AWS Glue, Apache Hudi, Apache Athena, AWS Lambda, and related services
• Strong proficiency in Python and SQL for data transformation, orchestration, and automation
• Solid understanding of data modeling, data architecture, and lakehouse patterns for analytics platforms and data-driven applications
• Experience supporting analytics platforms and data pipelines that enable advanced analytics or AI-enabled applications in production, with a clear focus on data quality, reliability, and performance
• Demonstrated ability to act as a technical lead, including solution design, code and model reviews, mentoring, and establishing best practices
• Strong problem-solving and analytical skills, with the ability to troubleshoot complex data and pipeline issues end to end
• Strong communication skills and the ability to work effectively with cross-functional technical and non-technical stakeholders
• Experience contributing to platform standards, documentation, and operational processes is a strong asset