Database Engineer

Database EngineerMost companies treat their database as plumbing — invisible until it bursts. We treat ours as the single source of truth that hundreds of fleets rely on to keep their vehicles, and their people, safe and moving.Do you think in schemas and see the elegant data model hiding inside a messy one? Do you get a little thrill from turning a query that took 40 seconds into one that takes 40 milliseconds? And have you already made AI a real part of how you work — not a buzzword on your resume, but a tool you reach for daily to design faster, debug smarter, and ship better? If so, read on…I'm the Manager of Infrastructure, Cloud Operations and SRE at RTA, and I'm looking for a Database Engineer to own the design, performance, and reliability of the data layer that powers our platform. We run a fleet of SQL Server databases across our customer base, increasingly on AWS, and the data they hold is the heartbeat of the product. But we're also growing — and we know that the data infrastructure we need tomorrow may look very different from what we have today.This is a high-ownership, high-impact role: you won't just tune indexes and respond to tickets — you'll help us understand what we need before we've figured it out ourselves, set the data architecture standard, drive migrations and modernization, and bring a modern, AI-augmented way of working to the whole team. If this sounds like you, you could be the peanut butter to our jelly. Read on and apply.This role is not for everyone. If you prefer clearly scoped tasks, minimal ambiguity, and steady incremental work, we'd still love to have you — just not in this seat. This role is for engineers who see an undefined data problem and feel a pull toward it, not away from it.What We're Looking ForIn general, someone who:Is a force multiplier. When you touch the data layer, the whole product gets faster, safer, and easier to build on. You raise the bar just by being in the room.Is passionate about serving others.Takes pride in their craft, finding fulfillment in data systems that are clean, fast, and trustworthy — and in never being the reason something broke.Has genuinely adopted AI into how they work. You use AI tools as part of your daily practice and have a real point of view on where they help, where they don't, and how to use them responsibly.Has strong opinions, loosely held. You'll push back when a design is wrong, but you update your views when shown better information. You're not the smartest person in the room — you just want to find the right answer.Is comfortable being part of a team that thrives on healthy conflict. People with thin skin need not apply. No, seriously.Passionately cares about our clients, who are fleet managers, parts clerks, and automotive technicians maintaining everything from squad cars to school buses — so everyone comes home safely at the end of the day.Thinks of themselves less, while not thinking less of themselves. You're other-centric, compassionate, and self-assured.Takes ownership and initiative — spotting the slow query, the missing backup, or the schema that's about to become a problem, and fixing it without waiting for a ticket.Loves to read, learn, grow, and stretch themselves. Bonus points for each book they've read by Patrick Lencioni!Specifically for This Job, Someone Who:Has 5–7+ years working hands-on with relational databases in production, with real depth in Microsoft SQL Server (T-SQL, query plans, indexing, partitioning). Experience with PostgreSQL is a strong plus.Knows relational and non-relational databases not just as concepts, but as tools — and has the judgment to know which belongs where. You can articulate why a relational model (SQL Server, PostgreSQL, Aurora) is the right choice for transactional, integrity-driven workloads, and equally why a document store (MongoDB, DynamoDB), a wide-column store (Cassandra, DynamoDB), a graph database (Neptune), or a key-value cache (Redis, ElastiCache) is the better fit for a different class of problem. You've made those calls in real systems, not just in theory, and you've lived with the consequences of getting it right — and wrong.Has meaningful hands-on experience with AWS database and data platform services, including but not limited to:Relational: Amazon RDS (SQL Server, PostgreSQL, MySQL), Amazon AuroraNon-relational / NoSQL: Amazon DynamoDB, Cassandra, Amazon ElastiCache (Redis/Memcached), Amazon NeptuneData warehousing: Amazon Redshift, and familiarity with alternatives like Snowflake or DatabricksData lake technologies: Amazon S3 as a data lake foundation, AWS Glue (ETL and data catalog), Amazon Athena (serverless query), AWS Lake FormationData pipelines: AWS Glue, Amazon Kinesis (streaming), AWS Data Pipeline, and/or third-party tooling such as dbt, Apache Airflow, or FivetranData quality: Experience implementing and enforcing data quality frameworks — validation, lineage, anomaly detection, and governance tooling such as AWS Glue Data Quality, Great Expectations, or similarUses AI in their daily work and isn't shy about it. You're fluent with modern AI tooling — coding assistants, LLM-based query and schema helpers, agentic workflows — and you actively apply them to design, optimization, documentation, and troubleshooting. You can also reason about the data layer behind AI features: vector storage, retrieval/RAG, and how operational data feeds analytics and ML.Designs data models, not just tables. You're fluent in normalization and when to denormalize, and you can defend those tradeoffs under pressure. Comfort with data modeling tools (ERwin, dbdiagram, Lucidchart, or similar) is expected.Lives and breathes performance tuning — execution plans, indexing strategy, query optimization, statistics, locking/blocking, and establishing performance baselines before there's a fire.Owns high availability and disaster recovery — backup/restore strategy, replication, Always On availability groups, failover, RPO/RTO targets, and the discipline to actually test recovery.Treats data security as non-negotiable — authentication, authorization, least privilege, encryption at rest and in transit, and an awareness of compliance obligations.Automates the boring stuff — maintenance, monitoring, and routine operations — using scripting (T-SQL, PowerShell, Python, or Bash) and infrastructure-as-code where it fits.Treats observability as a first-class concern, using tools like New Relic, Datadog, or native AWS monitoring to catch degradation before clients feel it.Communicates with precision. You can walk a senior engineer through a thorny migration plan and explain to a product manager why a schema change matters — without losing either of them.Has led before — bonus, not required. Time as a team lead, tech lead, or people manager is a real plus; we'd love someone ready to mentor and set standards, whether or not they want the management title back.Key ResponsibilitiesArchitect & Own the Data Layer: Design scalable, secure, well-documented database schemas and data architecture that the rest of engineering builds on with confidence. You set the direction, not just implement it — and you help us see around corners we haven't looked around yet.Guide Technology Selection: When a problem walks through the door, you know whether it needs a relational database, a document store, a graph, a cache, a warehouse, or a lake — and you can make that case clearly to engineers and non-engineers alike.Work AI-First: Bring AI into the team's daily workflow — using it to accelerate schema design, query optimization, documentation, and troubleshooting — and help establish what responsible, effective AI-assisted database work looks like at RTA.Build and Evolve Our Data Platform: Lead the design and implementation of data pipelines, data quality frameworks, and data lake or warehouse capabilities on AWS as our analytics and reporting needs grow.Performance & Scalability: Proactively find and fix bottlenecks — at the query, index, schema, and configuration level — so our systems stay fast as we grow.High