Senior Analytics Engineer
Team: Product & Engineering
About Mimecast:
At Mimecast, we are dedicated to elevating our teams and delivering the best version of ourselves to our customers and partners. Our Product & Engineering teams are at the heart of innovation, driving the development and delivery of secure, scalable, and reliable solutions for our global client base.
Mimecast is an AI-First engineering organization. Our teams actively leverage AI-powered development tools across all facets of engineering, from code development to testing, documentation, and operations. We're looking for leaders who don't just use AI tools but champion their adoption and establish new ways of working. Our AI leadership extends beyond how we build to what we build. Our Mihra AI agent delivers 7x faster threat response for customers, and we're recognized as "Agents of Change" in Human Risk Management. Engineers here work at the intersection of cutting-edge AI tooling and AI-powered security products that protect organizations worldwide.
Role Overview:
As Senior Analytics Engineer, you will design, build, and maintain scalable data pipelines and analytics infrastructure that powers Mimecast's product analytics capabilities from the integrity of source data to the metrics our executive team uses to run the business. You’ll be acting as a trusted thought partner to Product, GTM, and Finance leadership.
This is a high-leverage role: your work will directly shape how Mimecast measures product adoption, retention, and customer health, and how leaders make decisions about where to invest. We are looking for someone who treats analytics as a product with users, SLAs, a roadmap, and a quality bar and who can translate ambiguous business questions into durable data assets that compound in value over time.
Key Responsibilities:
- KPI & Metric Layer Ownership: Own the definition, modeling, and stewardship of Mimecast’s core product and commercial KPIs, including utilization, adoption, ARR, ARPU, NRR, and churn. Establish a single source of truth in the semantic layer, reconcile competing definitions across functions, and govern metric changes through a clear review and versioning process.
- Executive Reporting & Decision Support: Partner directly with the Director of Product Analytics, VP and C-level stakeholders to design the reporting that runs the business, board decks, QBRs, weekly product reviews, and executive dashboards. Translate ambiguous strategic questions into defensible analyses backed by well-modeled data.
- Analytics Architecture & Platform: Architect and evolve the dbt project on Snowflake, including staging, intermediate, mart, and exposure layers. Set conventions for naming, testing, documentation, and CI/CD, and drive performance and cost optimization across the warehouse. Own integration patterns from Salesforce, product telemetry, and finance systems into the analytics layer.
- Data Quality, Trust & Governance: Define and enforce the data quality bar: tests, freshness SLAs, lineage, incident response, and stakeholder communication when things break. Build the trust that allows executives to act on the numbers without second-guessing them.
- Mentorship & Technical Leadership: Act as the technical lead for the Analytics Engineering function. Mentor Analytics Engineer II’s through code review, pairing, and design feedback. Raise the bar in hiring, onboarding, and career development. Set the standards the rest of the team grows into.
- Cross-Functional Influence: Operate as a peer to senior leaders in Product, GTM, Finance, and Customer Success. Identify analytics gaps before stakeholders ask, propose the roadmap to close them, and influence prioritization across teams. Represent analytics in roadmap and planning conversations.
- Self-Service Enablement: Design Tableau and semantic-layer experiences that scale insight beyond the analytics team clear, well-governed, and trusted enough that PMs, CSMs, and finance partners can answer their own questions confidently.
Qualifications:
- 8+ years of experience in analytics engineering, data engineering, or BI engineering, with at least 2 years operating at a senior or lead level in a SaaS or technology environment.
- Expert SQL proficiency including advanced window functions, performance tuning, and modeling patterns for large fact and dimension tables in Snowflake. Experience with Amazon Athena (Presto/Trino) a plus.
- Deep Snowflake expertise warehouse sizing and cost management, RBAC, clustering, materialization strategy, query profiling, and Snowpipe/Streams/Tasks.
- Production dbt experience at scale multi-layer project design, macros, exposures, snapshots, tests, packages, and integration with CI/CD (GitHub Actions or equivalent).
- Strong Tableau skills or similar data visualization tools including LOD expressions, performance optimization, and governance of published data sources and dashboards.
- Proven track record designing and operating a semantic or metrics layer used by executives, with the judgment to navigate competing metric definitions across Product, Finance, and GTM.
- Experience modeling SaaS commercial data ARR, ARPU, retention, churn, expansion and reconciling product telemetry with Salesforce and finance source systems.
- Track record of mentoring Analysts and raising the bar on a team.
- Excellent written and verbal communication; able to brief a VP in five minutes and a board member in one slide.
- Bias toward treating analytics as a product: clear users, SLAs, versioning, and a roadmap.
Nice to Have:
- Amazon Athena migration to Snowflake expertise.
- dbt Cloud, Monte Carlo, or similar observability tooling experience.
- Proficiency in Python or similar languages.
#LI-GK1
Belonging at Mimecast
Cybersecurity is a community effort. That’s why we’re committed to building an inclusive, diverse community that celebrates and welcomes everyone – unless they’re a cybercriminal, of course.
We’re proud to be an Equal Opportunity and Affirmative Action Employer, and we’d encourage you to join us whatever your background. We particularly welcome applicants from traditionally underrepresented groups.
We consider everyone equally: your race, age, religion, sexual orientation, gender identity, ability, marital status, nationality, or any other protected characteristic won’t affect your application.
Due to certain obligations to our customers, an offer of employment will be subject to your successful completion of applicable background checks, conducted in accordance with local law.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.
Skills Required
- 8+ years in analytics engineering, data engineering, or BI engineering with at least 2 years at a senior/lead level in SaaS/technology
- Expert SQL proficiency including advanced window functions, performance tuning, and modeling patterns for large fact and dimension tables
- Deep Snowflake expertise including warehouse sizing, cost management, RBAC, clustering, materialization strategy, query profiling
- Production dbt experience at scale: multi-layer project design, macros, exposures, snapshots, tests, packages, CI/CD integration
- Strong Tableau skills or similar visualization tools, including LOD expressions, performance optimization, and governance
- Proven track record designing and operating a semantic/metrics layer used by executives, reconciling competing metric definitions
- Experience modeling SaaS commercial data (ARR, ARPU, retention, churn, expansion) and reconciling product telemetry with Salesforce and finance systems
- Track record of mentoring analysts and raising team standards through code review, pairing, and design feedback
- Excellent written and verbal communication; able to brief senior leaders and board members concisely
- Bias toward treating analytics as a product: users, SLAs, versioning, roadmap
- Amazon Athena (Presto/Trino) migration to Snowflake expertise
- dbt Cloud, Monte Carlo, or similar observability tooling experience
- Proficiency in Python or similar languages
Mimecast Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Mimecast and has not been reviewed or approved by Mimecast.
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Fair & Transparent Compensation — Pay is characterized as solid and competitive relative to similar companies, with a wide range of role-based salary outcomes. Compensation satisfaction is reinforced by frequent references to pay feeling fair for the work in multiple contexts.
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Healthcare Strength — Health insurance is repeatedly described as a strong part of the package, often paired with other core benefits. The overall benefits bundle is framed as comprehensive and supportive of employee and family health needs.
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Wellbeing & Lifestyle Benefits — Workplace perks such as flexible hours, hybrid/remote options, free meals, and on-site fitness amenities are consistently presented as meaningful additions to total rewards. These perks appear to materially improve day-to-day employee experience beyond base pay.
Mimecast Insights
What We Do
Relentless protection. Resilient world. Mimecast (NASDAQ: MIME) was born in 2003 with a focus on delivering relentless protection. Each day, we take on cyber disruption for our tens of thousands of customers around the globe; always putting them first, and never giving up on tackling their biggest security challenges together. We are the company that built an intentional and scalable design ideology that solves the number one cyberattack vector – email. We continuously invest to thoughtfully integrate brand protection, security awareness training, web security, compliance and other essential capabilities. Mimecast is here to help protect large and small organizations from malicious activity, human error and technology failure; and to lead the movement toward building a more resilient world. Learn more about us at www.mimecast.com.
