PeakMetrics - Senior Data Architect
Requirements
• At least eight years of experience working with production data systems. • Experience designing end-to-end data architectures across streaming, batch, and analytical workloads. • Strong understanding of creating data models that preserve signal from unstructured or semi-structured sources. • Ability to define clear contracts between raw, derived, and model-ready data for machine learning workflows. • Establishing standards for schema evolution, dataset versioning, reproducibility, and data quality assurance. • Experience with streaming platforms such as Kafka or AWS Kinesis (Preferred). • Familiarity with data orchestration tools like Temporal, Dagster, or Airflow (Preferred). • Knowledge of cloud-native data stacks, specifically AWS preferred experience. • Understanding and application of feature reuse, temporal consistency, leakage prevention in architectural decisions. • Fluent communication skills to document and communicate architectural decisions across teams effectively. • Experience with SQL for database interactions. • Proficiency in Python programming language. • Familiarity with machine learning workflows and feature engineering concepts. • Practical experience training AI Models, testing models for domain optimization. • Ability to work collaboratively in a remote environment setting.
Responsibilities
• Design data models that preserve signal from unstructured and semi-structured sources • Enable machine learning workflows by defining clear contracts between raw, derived, and model-ready data • Establish standards for schema evolution, dataset versioning, reproducibility, and data quality • Partner closely with engineering teams to improve scalability, reliability, and cost efficiency • Embed applied data science considerations into architectural decisions, including feature reuse, temporal consistency, and leakage prevention • Document and communicate architectural decisions clearly across teams • We would love to hear from you if: • You have eight or more years of experience working with production data systems • You have designed data architectures, not just individual pipelines • You have strong experience with streaming platforms such as Kafka or AWS Kinesis (Preferred) • You have experience with data orchestration tools such as Temporal (Preferred), Dagster or Airflow • You have experience with cloud-native data stacks, AWS preferred • You understand data modeling for analytics and machine learning use cases • You have working knowledge of machine learning workflows and feature engineering concepts • You have experience training AI Models and testing models for domain optimization • You are fluent in SQL and Python • You communicate clearly and collaborate effectively across teams in a remote environment • It would be nice if you had: • Experience supporting NLP or narrative intelligence systems • Familiarity with Athena, Parquet, Iceberg, or similar analytical storage technologies • Experience designing data systems for noisy or adversarial data environments • Experience supporting the data needs of machine learning teams
Benefits
• 1. Salary or Compensation Package (base salary, bonuses, equity options) • 2. Paid Time Off (PTO balance, vacation days allowed per year) • 3. Health Insurance Benefits (coverage details for the employee and their family members) • 4. Additional Perks or Employee Programs (such as wellness programs, gym discounts, etc.) • 5. Remote Work Options or Flexibility Policies (ability to work from home, telecommuting options) • 6. Retirement Plans or Savings Contributions (401(k), pension plans, matching contributions) • 7. Professional Development Opportunities (continuing education allowances, conference stipends, etc.) • 8. Work-Life Balance Initiatives (flexible scheduling options, compressed workweeks, sabbaticals) • 9. Employee Assistance Programs or Counseling Services (for personal and professional support) • 10. Parental Leave Policies (maternity/paternity leave duration and pay) • 11. Transportation Benefits (parking allowances, transit passes, commuter benefits programs) • 12. On-Site Amenities or Facilities (cafeteria services, fitness centers, childcare facilities) • 13. Job Security Measures (tenure track status, contract renewal policies) • 14. Work Environment and Culture (team dynamics, inclusivity initiatives, diversity programs) • 15. Performance Bonuses or Incentives (based on individual, team, or company performance metrics) • 16. Employee Recognition Programs (annual awards ceremonies, employee of the month/year acknowledgments) • 17. Workplace Safety and Health Standards (OSHA compliance, ergonomic workstations, mental health resources) • 18. Dress Code or Uniform Policy (if applicable to endocrinology clinic setting) • 19. On-Site Medical Services (for immediate medical attention in case of emergencies at the workplace) • 20. Professional Networking Opportunities (mentorship programs, industry events participation)
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