Sr. Database Engineer, Modernization

Innovation Associates, Inc.

4h • $116,000 - $185,000

About The Position

The Role:

Design and own the data layer for the NEXiA platform modernization — a customer-deployed pharmacy automation system running on SQL Server. This is a database developer and data modeler, not an analytics or data platform role. The mission: re-architect the data storage underneath a legacy application being incrementally decomposed, ensuring the data design reflects real pharmacy workflows rather than the accumulated technical debt of the current schema.

This person lives in the database but understands the application. They design schemas that enforce domain invariants, model the real relationships between pharmacy entities (medications, prescriptions, dispensing events, inventory), and make deliberate decisions about normalization, denormalization, key strategies, and temporal data patterns. They also introduce caching where it's never existed — identifying access patterns that demand it and implementing it cleanly.

The data design must support incremental modernization: legacy and modernized modules will coexist for an extended period, reading and writing overlapping data. This person designs the migration path so the application team can decompose the monolith without a big-bang data migration.

Requirements

• 8+ years of database development experience, with meaningful time spent on production transactional systems (not just reporting or analytics)
• Deep expertise in SQL Server — schema design, query optimization, execution plan analysis, indexing strategies, stored procedures
• Strong data modeling fundamentals: normal forms (through BCNF at minimum), denormalization strategies, temporal data patterns, key design (natural, surrogate, composite)
• Experience designing data layers for systems undergoing modernization or migration — not just greenfield schema design, but evolving a live schema under an active application
• Ability to model data around business domains: pharmacy, healthcare, logistics, manufacturing, or similar operationally complex domains where the data reflects real-world physical processes
• Systems thinking — understanding how data design decisions cascade into application behavior, query performance, and migration complexity
• Comfort reading and reasoning about application code (C#/.NET preferred) to understand data access patterns and domain intent

Nice To Haves

• Experience with caching technologies (Redis, Memcached) and cache invalidation patterns
• Experience with DDD and bounded context decomposition from a data perspective
• Healthcare, pharmacy automation, or regulated-environment experience (audit trail requirements, data retention, compliance)
• Familiarity with on-prem deployment constraints: customer-specific data configurations, upgrade migrations, backward-compatible schema changes
• Active use of AI-assisted tools for database development with concrete examples of impact — hands-on experience with Claude Code, Opus, or Sonnet is a significant plus

Responsibilities

• Design domain-aligned data models for modernized NEXiA bounded contexts, working closely with application engineers and domain experts to ensure schemas reflect real pharmacy workflows
• Model complex entity relationships: static reference records with volatile operational attributes (dispensing events, inventory positions, status changes, audit trails)
• Apply normalization rigorously where data integrity matters; denormalize deliberately where query performance demands it — and document the trade-off
• Design key strategies appropriate to each context: natural/composite keys for domain identity, surrogate keys where decoupling is needed, understanding when each approach creates problems at scale
• Own temporal data patterns: versioned records, slowly changing dimensions, point-in-time queries for audit and compliance
• Write and optimize complex SQL — stored procedures, views, functions, and queries against large transactional datasets
• Design and maintain indexing strategies; identify and resolve performance bottlenecks through execution plan analysis and query tuning
• Implement data migration scripts that move data from legacy schema to domain-aligned structures incrementally, without downtime
• Establish and enforce data integrity constraints at the database level — not just application-level validation
• Design and execute database performance tests — load testing against realistic data volumes, identifying bottlenecks before they hit customer environments
• Build automated validation for schema migrations: data integrity checks, constraint verification, rollback testing
• Establish performance baselines and regression detection for critical query paths
• Validate that migrations preserve data correctness across the old/new boundary — not just "it ran without errors" but "the data means the same thing it did before"
• Evaluate application access patterns and introduce caching where it provides meaningful performance improvement (Redis or equivalent)
• Design cache invalidation strategies that maintain data consistency with the source of truth in SQL Server
• Define caching boundaries that align with domain module ownership — prevent the cache from becoming another source of hidden coupling
• Partner with application engineers on the data implications of bounded context decomposition: which modules own which data, how do they share it, where are the seams?
• Design anti-corruption layers at the data level — views, APIs, or synchronization mechanisms that let legacy and modern modules coexist without schema entanglement
• Ensure data migration is incremental and reversible; big-bang data migrations are not an option with customers running the system daily
• Participate in domain modeling sessions to validate that data structures support (not constrain) the domain model
• Adopt AI-assisted tools for SQL optimization, schema analysis, data migration scripting, and test data generation
• Use AI tooling to accelerate understanding of the legacy schema — reverse-engineering implicit relationships, identifying dead columns, mapping actual usage patterns
• Demonstrate willingness to evolve tooling and workflows as AI capabilities mature; the database development workflow of 2027 won't look like today's
• Mentor application developers on data modeling principles — when to add an index vs. restructure a query, why normalization matters, how to think about data ownership
• Establish database development standards: naming conventions, migration patterns, review processes for schema changes
• Provide technical guidance that helps the broader team make better data decisions without creating a bottleneck

Benefits

• Generous time off policy that allows you to put your family first
• Opportunity to work on the cutting edge of pharmacy automation in a high growth tech company
• Competitive benefits, salary, and talent development opportunities
• Commitment to professional development and working for a company where your voice is heard

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed