Database Architect

We are seeking a highly skilled Database Architect to join our development team. This role goes beyond traditional database management—you will design scalable, high-performance database systems that seamlessly integrate with AI and ML components. The ideal candidate will have deep expertise in database architecture while also contributing to broader development efforts, including data pipelines for machine learning models, AI-driven analytics, and automated data processing. This position is crucial for building a robust foundation that supports our platform's data-intensive and intelligent features.

Responsibilities

- Design and architect database schemas, structures, and systems optimized for large-scale data ingestion, storage, and retrieval in a fintech-like environment.

- Collaborate with cross-functional teams (including AI/ML engineers, data scientists, and software developers) to integrate databases with ML/AI workflows, ensuring efficient data flow for model training, inference, and real-time analytics.

- Develop and maintain data pipelines, ETL processes, and data lakes to support AI-driven automation, reconciliation, and predictive modeling.

- Optimize database performance, scalability, and security to handle high-volume, mission-critical data while minimizing latency for AI applications.

- Evaluate and implement emerging technologies in databases, big data, and AI integration to enhance system capabilities.

- Conduct data modeling, normalization, and validation to ensure data integrity across automated financial processes.

- Provide technical guidance and support for development teams on database-related aspects of ML/AI projects, including feature engineering and data preprocessing.

- Troubleshoot and resolve complex data issues, ensuring compliance with industry standards for data privacy and security (e.g., GDPR, SOC 2).

- Contribute to the overall system architecture, including cloud migrations and hybrid environments.

Qualifications

- Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related field.

- 7+ years of experience in database architecture and design, with a proven track record in building scalable systems for data-heavy applications.

- Strong proficiency in relational databases (e.g., PostgreSQL, MySQL, Oracle) and NoSQL databases (e.g., MongoDB, Cassandra, Redis).

- Hands-on experience with cloud-based database services (e.g., AWS RDS, Aurora, DynamoDB; Azure SQL Database; Google Cloud Spanner, BigQuery).

- Solid understanding of big data technologies (e.g., Hadoop, Spark, Kafka) and data warehousing solutions (e.g., Snowflake, Redshift).

- Demonstrated experience supporting AI/ML development, including integration with frameworks like TensorFlow, PyTorch, or scikit-learn, and handling datasets for model training.

- Proficiency in programming languages such as Python, SQL, Java, or Scala for scripting, automation, and data manipulation.

- Knowledge of data governance, ETL tools (e.g., Apache Airflow, Talend), and API integrations for AI-enhanced systems.

- Excellent problem-solving skills and the ability to work in a fast-paced, agile environment.

Preferred Skills and Experience:

- Background in fintech, data automation, or SaaS platforms focused on data reconciliation and AI-driven financial automation.

- Experience with AI/ML-specific data tools, such as vector databases (e.g., Pinecone, Milvus) for embedding storage or graph databases for relationship modeling.

- Familiarity with containerization and orchestration (e.g., Docker, Kubernetes) for deploying database solutions in ML pipelines.

- Certifications in database management (e.g., Oracle Certified Professional, AWS Certified Database) or AI/ML (e.g., Google Cloud Professional Data Engineer).

- Strong communication skills to articulate technical concepts to non-technical stakeholders.