About this role
Full Time Data Engineer in healthcare at Codvo.ai in Maharashtra, Pune, India. Apply directly through the link below.
At a glance
- Work mode
- Office
- Employment
- Full Time
- Location
- Maharashtra, Pune, India
- Experience
- 5+ years
Core stack
- Data Engineering
- Infrastructure
- Generative AI
- Data Pipeline
- Performance
- Kubernetes
- PostgreSQL
- Prometheus
- Healthcare
- Analytics
- Feedback
- Grafana
- Python
- Docker
- Retail
- CI/CD
- ETL
- API
- S3
- ML
Quick answers
What skills are required?
Data Engineering, Infrastructure, Generative AI, Data Pipeline, Performance, Kubernetes, PostgreSQL, Prometheus, Healthcare, Analytics, and more.
Codvo.ai is hiring for this role. Visit career page
Pune, India
About us: Codvo.ai is a next-gen AI and engineering company helping global enterprises transform through Generative AI, Cloud-native platforms, and Product Engineering. With proprietary platforms like NeIO and Pulse, we’re enabling faster, smarter, and scalable digital transformation for industries including Energy, Retail, Travel, BFSI, and Healthcare.As we gear up to launch new AI-powered products and expand global presence, we are seeking a marketing leader to define how Codvo.ai influences the market, shapes perception, and creates a movement.
Role SummaryOwns the data pipeline from BMS ingestion through to the analytics layer. Responsible fordata reliability, quality, and the real-time streaming infrastructure that feeds the ML models.
ResponsibilitiesData Ingestion & StreamingBuild and maintain the BMS protocol bridge — OPC-UA, BACnet, and MQTT connectorsImplement the MQTT ingestion pipeline — topic subscription, message parsing, schemavalidation, TimescaleDB insertionMonitor ingestion health — message rates, latency, dropped messages, reconnectionlogicImplement the tag auto-mapping engine — pattern-based matching, confidence scoring,manual override workflowBuild the historian adapter — bulk data extraction from customer historian systems(AVEVA Historian, OSIsoft PI, InfluxDB) for baseline profilingData Storage & ManagementDesign and maintain the TimescaleDB schema — hypertables, continuous aggregates,retention policies, compressionImplement data partitioning strategy — per-tenant, per-site isolationBuild the training data snapshot pipeline — versioned dataset extraction with SHA-256checksums and manifest trackingManage the MinIO/S3 data lake — dataset storage, MLflow artifact storage, backupstrategyImplement data retention and archival policies per customer requirementsData Quality & MonitoringBuild the data quality monitoring pipeline — null rate tracking, stale timestampdetection, out-of-range value flagging, schema violation alertingImplement the tag freshness monitor — detect when sensors stop reporting, alert onstale dataBuild the data lineage tracking system — from raw BMS reading through featurecomputation through model predictionMonitor database performance — query latency, storage growth, index health,connection pool utilizationIntegrationBuild and maintain the CMMS integration — ServiceNow / Maximo API connectors, workorder creation, status syncImplement the feedback ingestion worker — poll CMMS for work order outcomes,match to predictions, update ground truth labelsBuild the Prometheus/Grafana metrics export pipeline — platform health metrics, dataquality dashboards
Expected Background5+ years in data engineering — ETL/ELT pipelines, streaming data, time-series databasesStrong Python skills, experience with PostgreSQL/TimescaleDB, MQTT, and messagebroker systemsExperience with Docker, Kubernetes, and CI/CD pipelinesFamiliarity with OPC-UA, BACnet, or industrial data protocols is a strong advantageExperience with data quality frameworks and monitoring
Role SummaryOwns the data pipeline from BMS ingestion through to the analytics layer. Responsible fordata reliability, quality, and the real-time streaming infrastructure that feeds the ML models.
ResponsibilitiesData Ingestion & StreamingBuild and maintain the BMS protocol bridge — OPC-UA, BACnet, and MQTT connectorsImplement the MQTT ingestion pipeline — topic subscription, message parsing, schemavalidation, TimescaleDB insertionMonitor ingestion health — message rates, latency, dropped messages, reconnectionlogicImplement the tag auto-mapping engine — pattern-based matching, confidence scoring,manual override workflowBuild the historian adapter — bulk data extraction from customer historian systems(AVEVA Historian, OSIsoft PI, InfluxDB) for baseline profilingData Storage & ManagementDesign and maintain the TimescaleDB schema — hypertables, continuous aggregates,retention policies, compressionImplement data partitioning strategy — per-tenant, per-site isolationBuild the training data snapshot pipeline — versioned dataset extraction with SHA-256checksums and manifest trackingManage the MinIO/S3 data lake — dataset storage, MLflow artifact storage, backupstrategyImplement data retention and archival policies per customer requirementsData Quality & MonitoringBuild the data quality monitoring pipeline — null rate tracking, stale timestampdetection, out-of-range value flagging, schema violation alertingImplement the tag freshness monitor — detect when sensors stop reporting, alert onstale dataBuild the data lineage tracking system — from raw BMS reading through featurecomputation through model predictionMonitor database performance — query latency, storage growth, index health,connection pool utilizationIntegrationBuild and maintain the CMMS integration — ServiceNow / Maximo API connectors, workorder creation, status syncImplement the feedback ingestion worker — poll CMMS for work order outcomes,match to predictions, update ground truth labelsBuild the Prometheus/Grafana metrics export pipeline — platform health metrics, dataquality dashboards
Expected Background5+ years in data engineering — ETL/ELT pipelines, streaming data, time-series databasesStrong Python skills, experience with PostgreSQL/TimescaleDB, MQTT, and messagebroker systemsExperience with Docker, Kubernetes, and CI/CD pipelinesFamiliarity with OPC-UA, BACnet, or industrial data protocols is a strong advantageExperience with data quality frameworks and monitoring
Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered.