Data Analytics Engineer

IXIS is a digital consultancy and technology company that empowers organizations to make smarter, faster decisions through strategy, technology, and analytics. They are seeking a senior-level Data Analytics Engineer to manage and evolve data ingestion, sanitation, and transformation pipelines, working with complex client data to create metrics and segments for data visualization products.

Responsibilities:

• Build enterprise-grade batch and real-time data processing pipelines using AWS with a focus on serverless architectures
• Design and implement automated ELT processes to integrate disparate datasets
• Work across multiple teams to ingest, extract, and process data via Python, R, zsh, SQL, REST, and GraphQL APIs
• Join and transform clickstream and CRM data into meaningful metrics and segments for visualization
• Create automated acceptance, QA, and reliability checks for business logic and data integrity
• Design appropriately normalized schemas and determine when to use SQL vs NoSQL solutions
• Optimize infrastructure and schema design for performance, scalability, and cost
• Help define and maintain CI/CD and deployment pipelines for data infrastructure
• Containerize and deploy solutions using Docker and AWS ECS
• Proactively identify and resolve data discrepancies and implement safeguards to prevent recurrence
• Contribute to documentation, onboarding materials, and cross-team enablement

Requirements:

• B.A./B.S. in Computer Science, Software Engineering, or a related field; training in statistics/mathematics/machine learning is a plus
• 3-5 years of experience building scalable, reliable data pipelines and data products in a cloud environment (AWS preferred)
• Deep understanding of ELT processes and data modeling principles
• Strong programming skills in Python (or similar scripting languages)
• Advanced SQL skills and intermediate to advanced relational database design experience
• Familiarity with joining large behavioral datasets like Adobe and GA4 clickstream data
• Excellent problem-solving skills and attention to data detail
• Experience managing and prioritizing multiple initiatives with minimal supervision
• Experience with dbt or other transformation-layer tools
• Familiarity with Docker containerization and orchestration
• Experience with statistical programming (R or Python preferred)
• API design or integration experience for data pipelines
• Experience developing in a Linux or Mac environment
• Exposure to data QA frameworks or observability tools (e.g. Great Expectations, Monte Carlo, etc.)