Database Administrator

About the Role

iPUMPNET ingests continuous real-time telemetry from sensors, motors, flowmeters, and energy meters across dozens and soon hundreds of pumping stations. The integrity, performance, and availability of that data is mission-critical. As Database Administrator, you will own and evolve PAPL's entire data storage landscape, from the embedded SQLite databases running on edge gateways in the field, to enterprise MS SQL Server instances, AWS S3 data lakes, and as PAPL's AI/ML roadmap matures Time-Series and Vector databases in the near future.

This is a broad, hands-on DBA role in a fast-scaling industrial technology startup. You will work at the intersection of field edge systems, cloud data platforms, and AI/ML data engineering ensuring that every layer of PAPL's data infrastructure is performant, reliable, secure, and ready for the next order of scale.

Key Responsibilities

1. Database Design, Administration & Optimization

• Own the design, deployment, administration, performance tuning, and lifecycle management of PAPL's database portfolio:

SQLite embedded databases on edge gateways and field data acquisition hardware for offline-first data buffering

– Microsoft SQL Server — core operational database for the iPUMPNET® cloud application platform

– AWS S3 — structured and semi-structured data storage, data lake organization, and archival tiers

– Time-Series DB (InfluxDB, TimescaleDB, QuestDB, OpenTSDB, Amazon Timestream, or Azure Data Explorer — evaluation and adoption roadmap)

– Vector DB (pgvector, Pinecone, Weaviate, or equivalent — evaluation and adoption roadmap for AI/ML embedding storage)

• Design and optimize database schemas for industrial IoT data patterns — high-frequency time-stamped sensor readings, asset hierarchies, alarm event logs, and operational KPIs.
• Tune query performance, indexing strategies, and execution plans for real-time and analytical query workloads.

2. Edge & Cloud Data Architecture

• Define and maintain data synchronization strategies between edge SQLite instances and the central MS SQL Server / cloud data platform — ensuring reliable, conflict-free, and bandwidth-efficient edge-to-cloud data flow.
• Design AWS S3 data lake partitioning, folder structures, and lifecycle policies for raw telemetry, processed data, model training datasets, and archival storage.
• Evaluate and recommend Time-Series database solutions aligned to iPUMPNET®'s real-time telemetry ingestion and long-horizon trend analytics requirements.
• Define Vector DB architecture to support AI/ML embedding storage for predictive maintenance models, semantic search, and RAG-based agentic AI features on the iPUMPNET® roadmap.

3. Data Reliability, Backup & Disaster Recovery

• Design and implement backup, recovery, and disaster recovery strategies for all database tiers — field edge, cloud application, and data lake.
• Define and monitor database availability SLAs appropriate for mission-critical utility monitoring operations.
• Implement and test failover, replication, and data integrity validation across cloud database tiers.

4. Security & Compliance

• Implement data-at-rest and data-in-transit encryption across all database tiers.
• Define and enforce role-based access control, audit logging, and data masking policies.
• Ensure database practices support PAPL's ISO and CMMI compliance roadmap.
• Aware of PAPL’s ISO-aligned management systems: Quality Management System (QMS – ISO 9001), Environmental Management System (EMS – ISO 14001), Information Security Management System (ISMS – ISO 27001), and Occupational Health & Safety Management System (OHSMS – ISO 45001).

5. Data Engineering Collaboration

• Partner with the Data Engineering and AI/ML teams to ensure database schemas, APIs, and storage structures are optimized for ETL/ELT pipelines, model training data preparation, and real-time inference data access.
• Define and maintain data dictionary, schema documentation, and data lineage records as per PAPL's technical documentation standards.

Required Qualifications & Experience

Education

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• Microsoft SQL Server certification (MCSA/MCSE Data Management, or equivalent) — preferred.
• AWS Database or AWS Solutions Architect certification — a plus.

Experience

• 10-15 years of database administration experience, with demonstrable depth in MS SQL Server and at least one cloud data store.
• Experience with embedded or edge database management (SQLite or equivalent) — IoT or industrial domain preferred.
• Hands-on AWS data services experience — S3, RDS, Glue, Athena, or Timestream.
• Exposure to Time-Series databases (InfluxDB, TimescaleDB, QuestDB, OpenTSDB, Amazon Timestream, Azure Data Explorer) — even evaluative experience is valued.
• Awareness of Vector databases and embedding storage concepts aligned to AI/ML applications — a growing advantage.
• Experience designing for high-frequency IoT or sensor telemetry data at scale.

Technical Skills

• Core: MS SQL Server, SQLite, T-SQL, query optimization, indexing, execution plan analysis
• Cloud: AWS S3, RDS, Glue/Athena; basic Azure SQL / Azure Data Explorer familiarity a plus
• Time-Series: InfluxDB, TimescaleDB, QuestDB, OpenTSDB, or Amazon Timestream (any one; willingness to learn others required)
• Vector DB: pgvector, Pinecone, Weaviate, or Chroma (familiarity or strong interest)
• Scripting: Python or PowerShell for automation, backup scripting, and data validation
• Data modelling: ER modelling, normalization, dimensional modelling for analytics
• Security: TDE, row-level security, audit logging, IAM-based database access controls

What Success Looks Like

Within 60 days, a complete database inventory and health assessment will be in place. Within 6 months, a Time-Series DB evaluation will be complete with a recommendation, edge-to-cloud sync reliability will have measurably improved, and backup/DR procedures will be documented and tested. Within a year, PAPL's data infrastructure will be production-ready for scaling to 10x current station counts, and a Vector DB architecture will be designed and ready for the AI/ML team to begin adopting.

What We Offer

• A rare opportunity to be part of building iPUMPNET® into a world-class Industrial IIoT SaaS platform.
• Exposure to an exceptionally diverse technology stack — cloud, AI/ML, industrial hardware, embedded systems, and field deployments.
• Direct collaboration with PAPL's technology leadership and genuine influence over product and platform decisions.
• Competitive compensation commensurate with experience.
• A culture that values innovation, continuous learning, patents, and real-world engineering impact.
• The chance to work on real-world deployments impacting water utilities, wastewater systems, and industrial facilities — systems that matter.
• Real-World Impact: You aren't just building another SaaS tool; you are solving the global water crisis by making pumping systems smarter and more efficient.
• Green-Tech Innovation: Lead the charge in reducing the carbon footprint of massive industrial utilities through AI-driven energy saving.