Data Engineer

Smarsh is a provider of compliance-focused data and communications solutions. The Data Engineer will serve as a database subject-matter expert, setting technical direction for scalable data infrastructure, performance, security, and real-time data systems. The role also involves leading cross-functional projects, mentoring engineers, reviewing changes, and proactively managing database health and risk.

Responsibilities:

• Serve as a subject-matter expert for database architecture and performance, guiding engineers through complex or ambiguous technical problems where established principles don't fully apply
• Set the technical approach for scalable data solutions, partnering with stakeholders across Product, Engineering, and Site Reliability to shape direction rather than simply implement it
• Drive larger, cross-functional projects from design through delivery, proactively identifying and addressing upstream and downstream impacts before they become issues
• Informally mentor other engineers, modeling sound engineering discipline and raising the technical bar for the wider team
• Shape how the team adopts modern engineering practices, including Agile methodologies, CI/CD pipelines, and DevOps principles
• Review code and database changes with an eye for systemic risk and performance, while also leading efforts to pay down technical debt across systems
• Track emerging technologies and make the call on which are worth adopting, guiding the team's direction
• Interpret database health and performance data (Splunk, Datadog, Grafana) to proactively flag and address risk before it surfaces as an incident

Requirements:

• Advanced, wide-ranging experience with database systems, with the judgment to operate independently in unfamiliar or ambiguous situations
• Deep expertise designing logical and physical data models, including schema design and data relationships, at a level others rely on for direction
• A demonstrated ability to set database performance strategy by tuning queries and indexes and establishing standards that others follow
• Strong command of data security practices (encryption, access controls, auditing), with the ability to shape how the team approaches them
• A track record of independently troubleshooting complex issues in systems that require high availability, serving as an escalation point for others
• Ability to plan for data growth and scale infrastructure with the full flow of work in mind, not just the immediate ask
• The credibility and collaborative instinct to influence peers and cross-functional partners, not just execute their requests
• Strong command of modern development practices and Agile methodologies, with a voice in how the team applies them
• Ability to communicate technical direction, trade-offs, and risk clearly to stakeholders outside your immediate team
• Comfort setting a course through evolving, ambiguous requirements rather than waiting for them to be resolved
• A proactive approach to identifying and solving problems that cross team boundaries
• 7+ years of hands-on experience with Microsoft SQL Server in a production environment, with demonstrated impact as a subject-matter expert
• Deep expertise in schema and data model design, including normalization, partitioning, and indexing strategy for both OLTP workloads
• Advanced proficiency in performance tuning, including T-SQL and stored procedure optimization, execution plan analysis, and query tuning at scale
• Proven, hands-on experience managing and troubleshooting SQL Server databases at terabyte scale, including tables with billions of rows, and sustained throughput exceeding 100k transactions per second
• Demonstrated ability to diagnose and resolve production capacity and contention issues at this scale, including lock/latch contention, TempDB performance, and transaction management
• Experience designing for horizontal as well as vertical scalability, anticipating data growth before it becomes a production risk
• Familiarity with NoSQL technologies (e.g., MongoDB, Cassandra, DynamoDB) is a plus
• Experience with cloud database platforms (e.g., AWS RDS/Aurora, MongoDB Atlas) is a plus