Senior Manager, Analytics Engineering
New
N
NextdoorNeighborhood technology
Location: US RemoteFull-TimeManager
SalaryThe starting salary for this role is expected to range from $204,000 to $301,000 on an annualized basis, or potentially greater in the event that your 'level' of proficiency exceeds the level expected for the role.
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Job Details
- Experience
- 10+ years in analytics engineering, data engineering, or analytics; 3+ years of people leadership experience
- Required Skills
- PythonSQLBusiness IntelligencePeople ManagementData modelingLooker
Requirements
- Bring 10+ years of experience in analytics engineering, data engineering, or analytics.
- Have strong hands-on SQL, Python, and data modeling expertise.
- Have 3+ years of people leadership experience, including hiring, coaching, and performance management in analytics or data teams.
- Define semantic layers and build scalable dashboards.
- Have experience with Looker or other BI and visualization tools, including tool selection, setup, and governance.
- Build and operate ETL/ELT pipelines with robust validation and monitoring.
- Maintain a high bar for data quality.
- Partner with cross-functional stakeholders to define KPIs and deliver measurable outcomes.
- Drive projects end-to-end and demonstrate clear outcomes and impact.
- Apply AI and emerging tools to improve productivity while maintaining rigor and decision quality.
- Bonus: experience with consumer products, user-generated content, and ad-supported business models.
- Bonus: proficiency with version-controlled code such as GitHub, and experience with agile or kanban workflows.
Responsibilities
- Develop and own the vision for Analytics Engineering, delivering curated datasets, dashboards, and internal data products.
- Define outcomes and communicate prioritization, progress, and impact to leadership.
- Establish standards for metrics, data governance, documentation, and SLAs, and oversee data quality through alerting and root-cause analysis.
- Build, coach, and elevate the analytics engineering team, setting clear goals.
- Collaborate with data science, finance, marketing, sales, product, and engineering to prioritize the roadmap and drive data-informed decisions.
- Scale data self-service and literacy through documentation, training, and enablement in tools and dashboards.
- Guide ETL/ELT pipeline architecture and operations, including code review and version control practices.
- Shift the team toward identifying data product needs and implementing solutions in phases.
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