Sr. Manager, Security — Continuous Monitoring v 2.0

p data-pm-slice= 1 1 [] RDQ227R1175 /p

p data-renderer-start-pos= 1648 While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered. /p

h2 class= PDq2pG_selectionAnchorContainer data-section-id= 1qw796 data-start= 142 data-end= 161 About Databricks /h2

p data-start= 163 data-end= 419 Databricks is the data and AI company. More than 12,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. /p

p data-start= 421 data-end= 589 Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. /p

h2 data-section-id= 1wcrfjg data-start= 591 data-end= 608 About the Team /h2

p data-start= 610 data-end= 812 The Continuous Monitoring (ConMon) team at Databricks builds and operates the engineering infrastructure that keeps Databricks' security control posture visible, measurable, and defensible at all times. /p

p data-start= 814 data-end= 1097 We build automation that continuously assesses whether security controls are actually working across cloud environments, SaaS platforms, identity systems, and enterprise applications — surfacing drift, coverage gaps, and control failures as they happen rather than at the next audit. /p

p data-start= 1099 data-end= 1304 Our work sits at the intersection of security engineering and GRC: we turn control requirements into code, evidence collection into automation, and security posture into intelligence that drives decisions. /p

p data-start= 1306 data-end= 1482 The ConMon team also maintains security tooling and scanning infrastructure to aid control evaluation. This includes scanning for secret leaks, asset vulnerabilities, and SAST. /p

p data-start= 1484 data-end= 1720 As Databricks' security program, cloud footprint, and regulatory obligations all scale, the Continuous Monitoring team is responsible for ensuring the company's control posture is always verifiable, always current, and never a surprise. /p

h2 data-section-id= 1froeg data-start= 1722 data-end= 1733 The Role /h2

p data-start= 1735 data-end= 1821 Databricks is looking for a strong data-start= 1763 data-end= 1781 Senior Manager /strong to lead the Continuous Monitoring team. /p

p data-start= 1823 data-end= 2096 You will own the engineering function that measures whether Databricks' security controls are working — across cloud infrastructure, identity, SaaS, and enterprise systems — and turns that measurement into something the Security organization, and its auditors, can rely on. /p

p data-start= 2098 data-end= 2411 That includes demonstrating control posture against the frameworks the business carries (SOC 2, ISO 27001, FedRAMP, PCI DSS, and emerging AI governance requirements), but the underlying question is broader: are the controls Databricks depends on actually in place and operating everywhere they are supposed to be? /p

p data-start= 2413 data-end= 2740 This is an engineering management role. The team writes production Python, operates data pipelines against cloud and SaaS APIs, and integrates with GRC and security tooling. Success is measured in control coverage, the accuracy and timeliness of what the team reports, and how quickly gaps get to the people who can close them. /p

p data-start= 2742 data-end= 2901 The role requires enough technical depth to review that work credibly, and enough judgement to prioritize the controls where measurement actually reduces risk. /p

p data-start= 2903 data-end= 3295 As the leader of Continuous Monitoring, you'll be responsible for growing and developing the team; setting a clear vision, priority, and strategy; making the case for the headcount and tooling the program needs; and building durable partnerships across GRC (SAC, SAF, Governance, Risk Management, TPRM), IT, Legal, and Engineering — the organizations that own the controls your team monitors. /p

h2 data-section-id= t7ztx5 data-start= 3297 data-end= 3324 The Impact You Will Have /h2

h3 data-section-id= 1dmtt75 data-start= 3326 data-end= 3363 Team Building People Leadership /h3

ul data-start= 3365 data-end= 3978

li data-section-id= 1rb2aey data-start= 3365 data-end= 3468 Hire strong Security Software Engineers who bring genuine engineering skill to compliance automation. /li

li data-section-id= qnslcp data-start= 3469 data-end= 3652 Support engineers in their career development with clear, specific feedback; develop senior ICs into technical leaders and grow the next generation of security engineering managers. /li

li data-section-id= 1wa6ozr data-start= 3653 data-end= 3871 Set and hold a high bar for engineering quality: code review standards, reliability and observability expectations for automation pipelines, and documentation that remains useful across audit cycles and team changes. /li

li data-section-id= 1ri6wdy data-start= 3872 data-end= 3978 Build a team that combines GRC domain knowledge with software engineering discipline, and hire for both. /li

/ul

h3 data-section-id= xaoa8s data-start= 3980 data-end= 4021 Control Measurement Program Ownership /h3

ul data-start= 4023 data-end= 4982

li data-section-id= 1vc2o3s data-start= 4023 data-end= 4238 Own the strategy and roadmap for Databricks' continuous monitoring platform — control state collection at cloud scale, continuous posture assessment, security and GRC tooling integration, and remediation tracking. /li

li data-section-id= rqit6z data-start= 4239 data-end= 4445 Define what the program measures and why: prioritize the controls whose failure would matter most to Databricks' security posture, rather than defaulting to the set a given framework happens to enumerate. /li

li data-section-id= 1qku7k2 data-start= 4446 data-end= 4661 Ensure coverage keeps pace with the business — new cloud environments, new products and services, new certifications and regulatory obligations, and AI governance controls that current tooling does not yet assess. /li

li data-section-id= 1pu4lgb data-start= 4662 data-end= 4817 Reduce manual evidence collection systematically; set and track targets for automated control coverage, freshness, and audit burden on Engineering teams. /li

li data-section-id= xn1e51 data-start= 4818 data-end= 4982 Own the accuracy of the team's output, including false positive rates; findings should be reliable enough that control owners act on them without re-verification. /li

/ul

h3 data-section-id= 1ruxqdl data-start= 4984 data-end= 5031 Posture Visibility Executive Intelligence /h3

ul data-start= 5033 data-end= 5695

li data-section-id= 1p7k18t data-start= 5033 data-end= 5195 Own the security posture dashboards, metrics, and reporting that give Security and GRC leadership an accurate, timely view of control health across the company. /li

li data-section-id= yjgd4v data-start= 5196 data-end= 5428 Define the metrics the program reports on — control coverage, drift, time-to-remediate, and where the organization is exposed — and build the reporting that leadership uses to make security investment and prioritization decisions. /li

li data-section-id= 1inugak data-start= 5429 data-end= 5572 Present the team's findings to senior leadership: control gaps and drift, their risk implications, and the effort required to remediate them. /li

li data-section-id= zld4g8 data-start= 5573 data-end= 5695 Make the same measurement data serve both audiences: evidence an auditor will accept, and signal the company can act on. /li

/ul

h3 data-section-id= 1xovhia data-start= 5697 data-end= 5741 Cross-Functional Partnership Execution /h3

ul data-start= 5743 data-end= 6918

li data-section-id= 1jv23kf data-start= 5743 data-end= 6010 Establish productive working relationships with GRC (SAC, SAF, Governance, Risk Management, TPRM), Enterprise Security, Product Security, Security Operations, IT, Legal, and Engineering leadership — the teams who own the controls, the evidence, and the remediation. /li

li data-section-id= 1rddl46 data-start= 6011 data-end= 6210 Partner with Enterprise Security and Product Security so control measurement reflects how systems are actually built and configured, and feed coverage gaps back to the teams who own those controls. /li

li data-section-id= 131y0sf data-start= 6211 data-end= 6394 Partner with SAC and SAF to ensure monitoring output meets auditor and 3PAO evidence standards, and with Governance to keep control monitoring aligned to policy as standards evolve. /li

li data-section-id= 1utmisp data-start= 6395 data-end= 6599 Coordinate with Risk Management and Security Operations on metric definitions and reporting boundaries so the organization gets one coherent picture of control health rather than three overlapping ones. /li

li data-section-id= amga4z data-start= 6600 data-end= 6782 Coordinate execution across teams to unblock cross-cutting initiatives: telemetry gaps in Engineering-owned systems, GRC platform migrations, and multi-quarter automation programs. /li

li data-section-id= n17i8l data-start= 6783 data-end= 6918 Make effective priority and resourcing decisions within the team; be accountable for defining and achieving the team's OKRs and KPIs. /li

/ul

h3 data-section-id= 1qma61v data-start= 6920 data-end= 6944 Technology Decisions /h3

ul data-start= 6946 data-end= 7749

li data-section-id= mx4zpn data-start= 6946 data-end= 7217 Lead build-vs.-buy evaluations for CSPM, SSPM, GRC automation, and security posture tooling; assess platforms on integration capability, measurement fidelity, maintenance cost, and fit with existing pipelines, and make the recommendation to Security and GRC leadership. /li

li data-section-id= 1m02p1p data-start= 7218 data-end= 7435 Keep the program's operating costs defensible — cloud spend, tooling, and the engineering time monitoring consumes — and be able to explain the tradeoff between monitoring coverage and what it costs to run at scale. /li

li data-section-id= 10mvjei data-start= 7436 data-end= 7749 Track what the program will need to measure next — changes in Databricks' architecture and threat exposure, new SOC 2 criteria, FedRAMP continuous monitoring changes, and AI governance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act) — and translate them into roadmap decisions early enough to build for them. /li

/ul

h2 data-section-id= 15t860x data-start= 7751 data-end= 7770 What We Look For /h2

ul data-start= 7772 data-end= 9435

li data-section-id= 3yyb94 data-start= 7772 data-end= 7864 2+ years of prior management experience leading Engineering or Security engineering teams. /li

li data-section-id= 1499g34 data-start= 7865 data-end= 8036 Typically 12+ years of experience, or an advanced degree plus 8 years, preferably focused on security engineering, security posture monitoring, or compliance automation. /li

li data-section-id= kp9yos data-start= 8037 data-end= 8245 Sufficient technical depth to review the work of the team: can read and critique Python automation, evaluate an integration architecture, and assess whether a monitoring approach will hold up in production. /li

li data-section-id= 1vld9hr data-start= 8246 data-end= 8511 Solid understanding of security controls and where they fail in practice — identity and access, configuration management, logging coverage, vulnerability management, and data protection — enough to judge whether a given measurement is telling you anything useful. /li

li data-section-id= 17ii7s1 data-start= 8512 data-end= 8670 Solid cloud security knowledge across at least one major platform (AWS, Azure, GCP) — IAM, audit logging, CSPM concepts, and cloud-native security services. /li

li data-section-id= 1beykfv data-start= 8671 data-end= 8851 Working knowledge of compliance frameworks (SOC 2, ISO 27001, FedRAMP, or equivalent) at a level sufficient to assess whether a control monitoring design will satisfy an auditor. /li

li data-section-id= 1emm89j data-start= 8852 data-end= 9045 Previous experience building security posture monitoring or compliance automation at scale, with attention to accuracy, coverage, and cost tradeoffs (experience with Databricks is preferred). /li

li data-section-id= nvf98x data-start= 9046 data-end= 9192 Focused on defining and driving efficiencies and improvements within the team; accountable for defining and achieving targets (e.g. OKRs, KPIs). /li

li data-section-id= 1319qc0 data-start= 9193 data-end= 9274 Makes effective priority decisions on resourcing and alignment within the team. /li

li data-section-id= 1ksv8og data-start= 9275 data-end= 9435 Strong communicator across technical, GRC, and executive audiences — can explain automation architecture to auditors and compliance requirements to engineers. /li

/ul

h2 data-section-id= 17i1yw5 data-start= 9437 data-end= 9452 Nice to Have /h2

ul data-start= 9454 data-end= 10240 data-is-last-node= data-is-only-node=

li data-section-id= 8d5vbz data-start= 9454 data-end= 9553 Experience with cloud security posture management (CSPM/SSPM) platforms at an architecture level. /li

li data-section-id= 18jev3i data-start= 9554 data-end= 9633 Experience with FedRAMP continuous monitoring programs at a leadership level. /li

li data-section-id= dkw1rd data-start= 9634 data-end= 9783 Hands-on background with GRC automation platforms (Vanta, Drata, ServiceNow GRC, Archer, or equivalent) at an implementation or architecture level. /li

li data-section-id= 1t8vrp0 data-start= 9784 data-end= 9879 Experience building automated monitoring for AI system controls and AI governance frameworks. /li

li data-section-id= 1lfmzfq data-start= 9880 data-end= 10014 Track record of building or scaling a security measurement or compliance engineering function from early-stage to mature operations. /li

li data-section-id= g20h1k data-start= 10015 data-end= 10148 Background in security operations, detection engineering, or security architecture that informs which controls are worth measuring. /li

li data-section-id= 1ksjnxm data-start= 10149 data-end= 10189 Experience with front-end development. /li

li data-section-id= 9lchdy data-start= 10190 data-end= 10240 data-is-last-node= CISSP, CISM, CISA, or equivalent certifications. /li

/ul div class= content-pay-transparency div class= pay-input div class= description p /p

p strong Pay Range Transparency /strong /p

p span style= font-weight: 400; font-size: 14px; Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page a href= https://www.databricks.com/sites/default/files/2024-08/us-pay-zone-mapping.pdf here /a . br /span /p

p /p /div div class= title Zone 1 Pay Range /div div class= pay-range span $228,600 /span span class= divider /span span $314,250 USD /span /div /div div class= pay-input div class= title Zone 2 Pay Range /div div class= pay-range span $205,700 /span span class= divider /span span $282,800 USD /span /div /div div class= pay-input div class= title Zone 3 Pay Range /div div class= pay-range span $194,200 /span span class= divider /span span $267,100 USD /span /div /div div class= pay-input div class= title Zone 4 Pay Range /div div class= pay-range span $182,900 /span span class= divider /span span $251,450 USD /span /div /div /div div class= content-conclusion p strong About Databricks /strong /p

p span style= font-family: arial, sans-serif; Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on a href= https://www.linkedin.com/company/databricks LinkedIn /a , a href= https://x.com/databricks X /a , a href= https://www.youtube.com/@Databricks YouTube /a , and a href= https://www.instagram.com/databricksinc/?hl=en Instagram /a . br br strong Benefits br br /strong /span At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click a href= https://docs.google.com/document/d/154un3e8Xav4BceOSlcYFZRGEuQI54xMxVydRwQn54eQ/edit?usp=sharing here /a . br br /p

p strong Our Commitment to Diversity and Inclusion /strong /p

p At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. /p

p strong Compliance /strong /p

p strong span style= font-weight: 400; If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone. /span /strong /p /div