Data Systems Engineer (DBA &ETL Focus)

Data Systems Engineer (DBA & ETL Focus) Location: Remote | Type: Full-Time | Department: Data & Infrastructure Work Hours: EST (Eastern Standard Time) Role Overview: We are looking for a Data Engineer with a strong ETL and DBA focus to design, manage, and optimize our data pipelines and database infrastructure. This role is crucial in ensuring that data is efficiently processed, stored, and made accessible for applications and analytics while maintaining compliance and performance. The ideal candidate will have hands-on experience with modern ingestion, transformation, and ontology tools within healthcare and financial data environments. Key Responsibilities: Develop and optimize ETL pipelines using tools such as Apache Airbyte, Mirth Connect, and custom parsers for structured and unstructured data. Design and maintain database infrastructure with PostgreSQL and Elasticsearch for high-performance search and retrieval. Implement schema inference and data profiling solutions using Pandas Profiling,genson, or Trifacta-like tools. Perform data validation and transformation leveraging Scikit-learn, Sentence-BERT, BioBERT, and Transformers-based NLP models for semantic matching. Ensure ontology and terminology consistency with UMLS API, OHDSI, SNOMED, and LOINC. Monitor, optimize, and secure data workflows, ensuring compliance with HIPAA, SOC 2, and other industry regulations. Develop automated monitoring tools for tracking pipeline performance and data integrity. Collaborate with the frontend and backend teams to ensure seamless data flow and integration within applications. Requirements: 3+ years of experience as a Data Engineer, ETL Developer, or DBA. Hands-on experience with ETL tools like Apache Airbyte, Mirth Connect, or equivalent ingestion frameworks. Strong experience in PostgreSQL and Elasticsearch, including query optimization, indexing, and database scaling. Experience working with semantic matching and data classification techniques using Scikit-learn, Transformers, and NLP models. Proficiency in Python for data processing (Pandas, SQLAlchemy, Airflow). Familiarity with healthcare and financial data ontologies such as UMLS API, OHDSI, SNOMED, and LOINC. Knowledge of cloud-based data storage solutions (AWS RDS, S3, Google BigQuery). Strong understanding of data security, compliance, and governance best practices. Bonus: Experience with data visualization and feedback UI (React + Flask/FastAPI backend). Cultural Fit: Agile, ambitious, and collaborative work environment. Ownership and accountability in small team settings. Strong communication skills for Slack, Teams, and email collaboration. Proactive mindset with a problem-solving attitude.