We are looking for a hands-on Senior Database Developer / Data Platform Engineer who can structure, store, govern, and expose the operational and analytical data foundation of our AMR software platform and Meta Planner. This role combines:
Responsibilities:
- Database design for real-time fleet control, order execution, configuration, analytics, and reporting.
- Data modeling for transport orders, AMR states, routes, missions, parameters, events, telemetry, and operational history.
- Reliable data access for backend services, algorithms, dashboards, and reporting use cases.
- Performance, consistency, scalability, data quality, and maintainability of critical data structures.
- Support for operational data, historical data, time-series data, configuration data, and analytics-ready datasets.
- Collaboration with Backend, Algorithm, Frontend/UI, DevOps, QA, Robotics, and Product teams.
Core Candidate DNA:
- Strong practical experience designing relational and/or time-series data models for production systems.
- Ability to structure complex operational data so that it is reliable, queryable, maintainable, and scalable.
- Understanding of transactions, indexing, partitioning, migrations, schema evolution, consistency, and query performance.
- Ability to distinguish between data needed for real-time control, configuration, history, analytics, and reporting.
- Understanding that poor data modeling creates long-term product and operational problems.
- Understanding that AMR systems require reliable, current, and consistent operational state.
- Ability to model data for orders, tasks, missions, robot states, route reservations, traffic status, alarms, user actions, and execution progress.
- Ability to design data access patterns that support fast decisions by backend services and planning algorithms.
- Understanding of trade-offs between normalized models, denormalized read models, caching, event logs, and analytics models.
- Ability to help separate control-critical data from reporting-heavy workloads to avoid performance and reliability problems.
- Ability to build a data foundation that supports configuration, product parameters, dashboards, analytics, KPIs, and customer reporting.
- Understanding of data lifecycle, retention, archival, backups, auditability, and traceability.
- Ability to create clean interfaces for backend services, frontend dashboards, analytics pipelines, and reporting tools.
- Ability to define data contracts, naming conventions, migration strategies, and ownership boundaries.
- Understanding of data quality, data validation, lineage, and governance as engineering topics.
- Ability to work closely with engineers and product teams to understand how data is created, changed, consumed, and interpreted.
- Ability to implement database-related backend logic, stored logic where appropriate, migrations, tests, and performance improvements.
- Understanding of deployment, observability, monitoring, and incident investigation for data-related issues.
- Ability to explain data model decisions and trade-offs to backend, algorithm, and product stakeholders.
- Preference for pragmatic, maintainable solutions over theoretically perfect but unusable models.
Required Skills:
- Strong experience with relational databases, ideally PostgreSQL.
- Excellent understanding of schema design, normalization, denormalization, indexing, constraints, transactions, and migrations.
- Ability to design data models for operational systems, configuration data, historical records, and analytics-ready datasets.
- Experience optimizing queries and diagnosing performance bottlenecks.
- Experience with data versioning, auditability, and traceability concepts.
- Understanding of data access patterns for real-time or near-real-time systems.
- Ability to model state, events, commands, configuration, telemetry, and historical execution data.
- Understanding of concurrency, consistency, idempotency, transactions, locking, and data integrity.
- Experience separating write models, read models, event logs, and reporting workloads where appropriate.
- Ability to support backend services and planning algorithms with reliable and fast data access.
- Ability to prepare structured data for dashboards, KPI visualization, analytics, and reporting.
- Understanding of historical data modeling, aggregations, materialized views, data marts, or similar reporting concepts.
- Ability to support metrics such as throughput, order lead time, robot utilization, traffic congestion, waiting time, errors, downtime, and SLA performance.
- Experience with data quality checks and validation rules.
- Ability to design datasets that are understandable and useful for Product, Operations, and Customer Success teams.
- Strong SQL skills and practical backend integration experience.
- Experience with Python, Rust, or another backend language.
- Git workflows, code reviews, automated tests, and CI/CD discipline.
- Experience with Docker and Linux-based development environments.
- Understanding of APIs, services, migrations, observability, backups, and deployment processes.
Daily Tasks:
- Design and evolve the data model for the AMR platform and Meta Planner.
- Structure data for transport orders, robot states, missions, routes, traffic status, alarms, configuration, parameters, users, permissions, and execution history.
- Ensure backend services and planning algorithms can access reliable data quickly and consistently.
- Separate operational data needs from analytics and reporting needs through appropriate schemas, read models, aggregations, or pipelines.
- Develop migration strategies and standards for schema evolution across product releases.
- Improve query performance, indexing, partitioning, retention, and data lifecycle management.
- Create analytics-ready structures for KPIs, dashboards, operational reporting, customer reporting, and algorithm evaluation.
- Support data quality, auditability, traceability, and root-cause analysis through structured event and history models.
- Work with DevOps on backup, restore, monitoring, observability, and production reliability of database systems.
- Work with Frontend/UI teams to provide clean and reliable data for dashboards, KPI views, and analytics screens.
- Work with Algorithm teams to provide high-quality historical and current data for planning, optimization, simulation, and machine learning use cases.
