The Opportunity
This is a senior database architecture role leading a major platform migration. Solvace is modernising its production platform from SQL Server to Aurora PostgreSQL — a strategic migration that touches every module, every client, and every data access pattern in the system.
This is not a simple lift‐and‐shift. The migration involves a fundamental multi‐tenancy model redesign (per‐client databases to a consolidated PostgreSQL architecture with Row‐Level Security), stored procedure porting, view dependency refactoring, and the design of migration tooling that enables module‐by‐module cutover with zero data loss and minimal downtime. The platform serves global manufacturing clients who depend on continuous availability.
This is an entirely forward‐looking role. The person joining will own the target database architecture and the migration path to get there. While the broader engineering team is modernising the application layer (.NET 8, containerisation, Kubernetes), this role owns the data layer transformation — the foundation everything else depends on. Collaboration with the platform engineering and AI teams is essential, but the day‐to‐day work is schema design, migration engineering, and performance optimisation, not application development.
This role is fully REMOTE.
Core Technical Requirements:
-PostgreSQL (deep) — this is the target platform. Must have expert‐level knowledge of: schema design, Row‐Level Security (RLS) policies, PL/pgSQL, partitioning strategies, indexing, query planning and EXPLAIN analysis, connection pooling (PgBouncer), vacuum tuning, and Aurora PostgreSQL‐specific features (read replicas, global databases, failover
-SQL Server (strong) — the source platform. Must understand: T‐SQL stored procedures, complex view hierarchies, SQL Server Agent jobs, cross‐database queries, and the idiosyncrasies of migrating T‐SQL patterns to PostgreSQL
-Database migration at scale — proven experience migrating production databases between engines (ideally SQL Server → PostgreSQL). Must understand: schema conversion, data type mapping, stored procedure porting, testing strategies, rollback planning, and cutover orchestration
-Multi‐tenant database architecture — experience designing tenant isolation patterns. The migration involves moving from a per‐client database model to a consolidated architecture with Row‐Level Security. Must understand the trade‐offs between schema‐per‐tenant, database‐per‐tenant, and RLS‐based isolation — including performance, security, and operational implications
-Entity Framework Core — the application uses EF Core for data access. Must understand how EF Core handles dual‐provider registration (SQL Server + PostgreSQL simultaneously), migrations, and how schema changes flow through the ORM layer
-Performance tuning — ability to analyse and optimise query performance across both SQL Server and PostgreSQL. Must ensure the PostgreSQL target matches or exceeds SQL Server performance for critical workloads
-Python or scripting proficiency — for building migration tooling, data validation scripts, and automated testing. Python is the preferred language across the AI and data teams
-Streaming data architecture (Kafka) — the database architecture must be fully integrated with the streaming data strategy. Must understand how transactional data flows into event streams via CDC (Debezium, Kafka Connect), how Kafka topics map to database change events during migration, and how the streaming layer supports real‐time data synchronisation between SQL Server and PostgreSQL during the coexistence period. Experience with Kafka or Confluent Cloud is required
-Linux / CLI proficiency — must be comfortable working in Linux environments and command‐line database tooling (psql, pg_dump, pgloader, etc.)
AI-Assisted Development Methodology
Solvace is transitioning towards AI‐assisted development as a core engineering practice. For the Database Architect:
-Hands‐on experience with AI coding tools — Claude Code, OpenAI Codex, GitHub Copilot, Cursor, or similar. Particularly valuable for accelerating stored procedure porting (T‐SQL → PL/pgSQL), generating migration scripts, and writing data validation queries
Spec‐driven migration — ability to write clear migration specifications per module that can be reviewed, tested, and partially automated with AI‐assisted tooling
-Portfolio evidence — professional projects or side projects demonstrating AI‐assisted development practices. Contributions to or experimentation with emerging projects like OpenClaw are a strong signal
-Testing rigour — experience building comprehensive data validation suites that verify migration correctness across large datasets
Nice-to-Have
-Go or Rust — valued as evidence of strong systems engineering, particularly for building high‐performance migration tooling
-AWS RDS expertise — the platform runs on AWS RDS (SQL Server) migrating to Aurora PostgreSQL. Experience with RDS parameter groups, Aurora scaling, PrivateLink, and cross‐region replication
-AWS DMS or pglogical — additional migration and replication tooling beyond Kafka‐based CDC
-Terraform / IaC — database infrastructure is managed via Terraform
- Databricks / data lakehouse — a lakehouse POC is being evaluated with Delta Sharing via PrivateLink to the database layer; understanding how analytics platforms connect to transactional databases is valuable
Manufacturing / industrial domain — understanding of the data patterns in operational excellence platforms (KPIs, inspections, action plans, root cause analysis)
-Regulatory and data residency — the multi‐tenancy redesign has legal and contractual implications around data isolation. Experience navigating data governance requirements is a plus
-pgloader or AWS SCT (Schema Conversion Tool) — specific migration tooling experience
Research vs. Applied
Entirely applied / production‐focused.
This is a hands‐on database architecture and migration role. The person needs to be comfortable making schema decisions, writing migration scripts, debugging query performance, and operating in a production environment where manufacturing clients depend on data availability.
Why Join ->
-Own a strategic platform migration end‐to‐end — this is not incremental database maintenance. It's a full architecture transformation: redesigning the multi‐tenancy model, porting the stored procedure layer, and building the migration tooling from scratch
-Architectural authority — this person defines the target database architecture for the entire platform. Schema design, tenant isolation strategy, performance patterns — they own it
Direct impact on every team — the database migration is the foundation that the application, AI, and data teams all depend on. This role unblocks and enables every other engineering workstream
-Modern PostgreSQL at scale — Aurora PostgreSQL with Row‐Level Security, partitioning, and connection pooling for a multi‐tenant manufacturing platform. Technically interesting PostgreSQL work, not just basic CRUD
-Manufacturing AI — the database architecture directly enables AI agent capabilities. The Data Engineer and AI Engineer both depend on clean, well‐governed data access patterns that this role defines
-Team leadership — the Data Engineer reports into this role, giving the Architect a team from day one and the opportunity to build out the data function as the organisation grows 7 -
-Greenfield migration design — no migration tooling exists. This person defines the approach, tools, and processes from scratch