• Work From Anywhere in LATAM and the Philippines • Design clean schemas, write efficient SQL, and optimize Postgres performance • Extend and evolve the Postgres schema across recruiting, candidates, and business operations • Build SQL views and derived tables for reporting and AI • Optimize query performance through indexing, partitioning, and execution-plan analysis • Complete schema audit with top-priority fixes shipped and measurable performance gains within the first 3-6 months

• Розробка та підтримка масштабованих ETL/ELT-процесів для збору, трансформації та завантаження даних • Проєктування та впровадження надійних пайплайнів даних для обробки інформації як у режимі реального часу (real-time), так і пакетної обробки (batch processing) • Забезпечення якості, цілісності та доступності даних для аналітичних і операційних систем • Оптимізація продуктивності SQL-запитів та архітектури баз даних • Автоматизація розгортання та моніторингу компонентів інфраструктури даних • Тісна співпраця з командами аналітики, розробки та бізнесу для впровадження рішень, заснованих на даних

• Develop, maintain, and optimize legacy ETL and DWH processes. • Work hands-on with Oracle, Teradata, SQL, PL/SQL, and stored procedures. • Develop IBM DataStage or similar ETL platforms such as Informatica or Ab Initio. • Participate in legacy-to-modern data migration projects. • Analyze existing data structures and map them to modern architectures. • Build and manage data pipelines using Apache Spark. • Read and write data from object storage environments such as AWS S3. • Contribute to technology evaluation, PoC studies, and future platform decisions. • Define technical standards and provide architectural guidance to data and analytical teams.

• Design, build, and maintain reusable feature datasets that support machine learning use cases including personalization, engagement, risk prediction, churn modeling, recommendation systems, and experimentation. • Establish self-service foundations that streamline and democratize dataset creation across the data organization. • Partner with Data Scientists to translate modeling requirements into production-ready feature pipelines, supporting the full model lifecycle from exploration to deployment. • Identify source data, transformations, and historical windows needed for feature engineering. Help define and build shared, reusable feature definitions across models rather than one-off datasets. • Balance features freshness, correctness, latency, and computational efficiency when designing data pipelines. • Build datasets that support both historical model training and future production inference. • Design and implement batch and streaming pipelines that transform raw healthcare, behavioral, product, and operational data into trusted ML-ready datasets. • Build reliable data processing systems using Python, SQL, Spark, and modern cloud data platforms. • Optimize large-scale distributed processing for performance, scalability, and cost. • Design data pipelines that are modular, testable, observable, and easy to evolve as product requirements change. • Ensure data quality through testing, anomaly detection, schema validation, and pipeline monitoring. • Partner with platform teams to support near real-time feature generation where appropriate. • Improve reproducibility by standardizing feature computation across experimentation and production. • Support rapid experimentation without sacrificing long-term maintainability. • Establish engineering standards for correctness, documentation, and maintainability. • Familiarity with feature stores or feature management platforms. • Familiarity with model training pipelines and MLOps workflows.