Healthcare data knowledge
SQL
Python
Data Warehousing
ETL
Snowflake and/or Big Query
AWS: Glue, Athena, Redshift(is a plus)
Azure: data factory, Synapse
Git
Frameworks & Libraries in Our Stack (Python Ingestion System)
Web Framework
- Flask 3.0 – Core web framework providing REST APIs and web dashboard capabilities
- Flask-Login – Session management and authentication decorators
- Authlib – Google OAuth 2.0 integration
- itsdangerous – Secure session token signing
Database
- psycopg2-binary – PostgreSQL adapter used for job queue, node registry, and user/role storage
- SQLAlchemy – ORM and connection URL builder supporting Oracle and SQL Server
- oracledb – Oracle database driver
- pymssql – SQL Server adapter for source data extraction
Google Cloud
- google-cloud-bigquery – BigQuery table creation, DDL operations, and streaming inserts
- google-cloud-storage – GCS uploads used for CSV chunk backups
- google-cloud-secret-manager – Secure credential retrieval
Data Processing
- pandas – DataFrame manipulation for Oracle export processing
- openpyxl – Excel support for report generation
Scheduling & System
- APScheduler – Background job scheduler for replication and validation jobs
- psutil – Process and system metrics
Configuration
- PyYAML – YAML configuration file parsing
Runtime Infrastructure
- Oracle Instant Client 21.12 – Native libraries required by the oracledb driver
- Python 3.11 – Base runtime
- Gunicorn – Production WSGI server
- Podman / Docker – Containerization
- Kuberenetes (not GKE flavor, using Gardener)
Required Engineer Skills
Backend Development
- Python 3.x development including threading, decorators, generators, and context managers
- Flask framework concepts including routing, middleware, blueprints, and request lifecycle
- REST API design and implementation
- SQL proficiency including transactions, row-level locking, and window functions
Databases
- PostgreSQL (GCP PAAS) including connection pooling, migrations, query optimization, and FOR UPDATE SKIP LOCKED patterns
- Oracle familiarity including connection configuration and SQL dialect differences
- SQL Server familiarity for source data extraction
Google Cloud Platform
- BigQuery including table creation, DDL/DML operations, schema management, and dataset usage/config/etc
- Google Cloud Storage including object uploads and bucket management
- Secret Manager usage (consumption), use in k8s
- Service account usage and Application Default Credentials (WIF/WIP)
- General k8s familiarity, as the stack is running on k8s
DevOps & Infrastructure
- Docker / Podman container builds and runtime management
- CI/CD pipelines such as Azure DevOps or similar platforms
- Environment variable and secrets management
- Distributed system concepts including heartbeats, distributed locking, and crash recovery (Kubernetes runtime)
Data Engineering
- ETL/ELT pipeline design (our framework)
- Chunked data processing for large dataset exports, particularly focused on partitioning workloads into scaled masses of worker nodes
- Note: This is VERY different from the existing wellstack implementation, this is not running on VMs, it’s running on kube so – obviously some level of understanding of distributed workloads is necessary
- Schema mapping between heterogeneous systems (Oracle or SQL Server to BigQuery)
- Row-count validation and data quality checks
Authentication & Security
- OAuth 2.0 and OpenID Connect authentication flows
- Role-based access control implementation
- Secure credential handling and Secret Manager integration
General Software Engineering
- Multithreading concepts including locking and thread-safe shared state
- Background job scheduling patterns
- Structured logging design and per-job log isolation
- Robust error handling and graceful degradation in distributed systems
- Database migration authoring and maintenance
очень желательно чтобьі работали раньше или в МедеАналитикс или в Визиквейте