Senior Data Engineer - Remote, Scalable AI Data Pipelines

Opinov8; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work

Learning opportunities

Diverse team collaboration

Job summary

Opinov8 is seeking a data-focused engineer to design core data models and build robust production data pipelines on AWS, establishing a single source of truth for key metrics in a sports-analytics context.

You will structure data for business insights, evaluate tools like BigQuery, Snowflake, and Databricks, and collaborate with Product, Engineering, AI, and Business teams to scale architectures and deliver value.

Qualifications

• 5–7+ years in data-heavy roles (data engineering/ML)
• Strong Python and SQL skills
• Hands-on production data pipelines experience
• AWS and cloud-native data architectures
• Familiar with CI/CD, monitoring, and deployments
• Excellent English communication
• Interest in sports analytics and performance data

Responsibilities

• Define and implement core data models (users, events, performance)
• Design and operate robust production-grade data pipelines
• Establish a single source of truth for key metrics
• Structure data for business and product analysis
• Build data services and jobs on AWS (S3, Lambda, ECS/EKS, Glue, Athena, Redshift)
• Evaluate and introduce tools (BigQuery, Snowflake, Databricks)
• Ensure architecture scales with product, AI, and data growth
• Write clean, well-structured Python code following best practices
• Collaborate with Product, Engineering, AI, and Business teams

Skills

Python programming

SQL queries

AWS

Data pipelines

CI/CD

Monitoring & logging

English communication

Sports analytics

Tools

BigQuery

Snowflake

Databricks

Job description

Opinov8 is seeking a data-focused engineer to design core data models and build robust production data pipelines on AWS, establishing a single source of truth for key metrics in a sports-analytics context.

You will structure data for business insights, evaluate tools like BigQuery, Snowflake, and Databricks, and collaborate with Product, Engineering, AI, and Business teams to scale architectures and deliver value.