Design and maintain scalable, high-performance database solutions to support data science workflows and ML experimentation
Partner with data scientists to understand data access patterns and develop storage strategies that accelerate analysis and model training
Serve as the internal subject matter expert on PostgreSQL —including schema design, indexing, partitioning, and query optimization
Evaluate and integrate alternative database technologies (e.g., MongoDB, Neo4j, Redis, Cassandra) where they provide clear advantages
Lead efforts to optimize data pipelines for both structured and unstructured data used in algorithm development
Ensure data integrity, security, and governance across storage systems
Implement monitoring, automation, and performance-tuning tools for all database environments
Advise on data lifecycle management—balancing accessibility for R&D with efficiency and compliance requirements

5+ years of experience in data engineering, database architecture, or related technical roles
Expert-level proficiency in PostgreSQL (query tuning, schema design, indexing, partitioning, replication)
Strong understanding of data modeling, normalization vs. denormalization tradeoffs, and query optimization
Experience with non-relational databases (e.g., MongoDB, Cassandra, Neo4j, Redis, or DynamoDB)
Familiarity with machine learning workflows and how data is consumed for training, evaluation, and deployment
Experience with cloud database services (AWS RDS/Aurora, GCP Cloud SQL, Azure Database)
Proficiency in SQL and one or more scripting languages (Python preferred)
Excellent communication and collaboration skills

Data Engineer – Data Architecture, Data Science, Machine Learning

Key skills