Data Engineer
As a Database Engineer on the Professional Archive team, you'll operate as a subject-matter expert for the data infrastructure that powers our platform – driving the architecture, scaling, and reliability decisions that keep high-velocity, compliance-critical systems performant and secure. You'll work with SQL Server environments spanning multi-terabyte data volumes, tables with billions of rows, and sustained throughput exceeding 100,000 transactions per second.
You'll work independently through ambiguous and unfamiliar problems, set technical direction for real-time data pipelines and system performance, and act as a go-to resource for engineers across the wider organization. Your work will directly shape how Smarsh clients meet their compliance needs through scalable, dependable data and search capabilities.
You'll partner with leaders across Product Management, Engineering, and Site Reliability, weighing a range of inputs to resolve complex challenges and influence strategy beyond your immediate team. We're looking for someone who brings deep technical judgment, mentors others, and is motivated to make an impact through thoughtful, high-leverage engineering.
How will you contribute?
- 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
What will you bring?
We're looking for a recognized subject-matter expert who operates independently, drives clarity through ambiguity, and raises the bar for those around them.
- 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
Preferred Qualifications
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If you don't meet every qualification listed below, we still encourage you to apply. We value diverse experiences and perspectives. Progression to this level reflects demonstrated ability to perform as a subject-matter expert, not years of experience alone.
- 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
What do we offer?
- Healthcare insurance: We provide medical, dental, and vision insurance, and a flexible spending account that allows you to set aside pre-tax dollars to pay for eligible out-of-pocket expenses.
- Stock options.
- Personal time off: A healthy work-life balance is critical to your success at the office. Smarsh offers a “take-what-you-need” time off policy as well as flexible work arrangements.
- 401K Match: Smarsh provides a 4% 401K match for which employees are fully vested on day one.
- Sabbatical: The Smarsh sabbatical programme provides a time to recharge, study or simply do something you are passionate about away from the workplace. Employees are eligible after six years of service.
- Recognition: We’re big on kudos for a job well done. Our employee-recognition programme enables co-workers to nominate their peers who best embody our core values for recognition.
Skills
As published by lever · 7 questions · 6 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL, Other website
Pick from a list (1)
- Will you require visa sponsorship now or in the future? optional
Written answers (6)
- How many years of hands-on experience do you have with Microsoft SQL Server in production? Describe the largest environments you have managed — specifically the data volumes, table row counts, and transaction throughput you have operated at.
- Describe a SQL Server performance problem you diagnosed and resolved at scale — for example, lock or latch contention, TempDB pressure, or a query degrading under high concurrency. What was the symptom, what did you find, and what did you change?
- Tell us about a time you served as the escalation point for a complex database incident that others could not resolve. What made it complex, how did you diagnose it, and what was the outcome?
- Describe your approach to schema and data model design for high-throughput OLTP workloads. Walk us through a specific design decision — including normalisation, partitioning, or indexing strategy — that you made at scale and the trade-offs you considered.m leverages an AI-first mindset. How do you currently use AI tools (like GitHub Copilot, Claude, or ChatGPT) to improve your engineering productivity or database troubleshooting?
- This role involves setting technical direction and mentoring other engineers, not just executing technical tasks. Describe a time you raised the technical bar for a team — either through establishing a standard, changing an approach, or developing another engineer's capabilities.
- How do you approach capacity planning for a database system experiencing sustained, high-velocity data growth? Describe a specific situation where you anticipated and addressed a capacity risk before it became a production problem.
