• Consolidate and link data across partner APIs, internal databases, and partner-facing portals to build a coherent picture of program performance • Build and maintain dashboards (in Redash or similar) covering registration numbers, enrollment funnel analyses, and other key program metrics • Manually collect interval usage data from after demand response events, and help develop processes to make this collection faster and more reliable over time • Identify gaps, inconsistencies, and opportunities for automation in our residential data pipelines, and flag them so we can fix them

• Own recurring reporting across Sales, Marketing, and CS: pipeline, win rates, ARR, funnel performance, churn/retention metrics, and usage data • Build and maintain reporting infrastructure in Looker Studio, BigQuery, and Salesforce • Ensure reports are accurate, timely, and accessible to the right stakeholders • Identify trends and surface insights proactively — don't wait to be asked • Design and build standard and mid-complexity dashboards for GTM stakeholders • Collaborate with the Senior Director on architecture for high-visibility exec-facing dashboards • Establish dashboard standards: naming conventions, refresh cadences, data source documentation • Maintain a self-service reporting culture — build dashboards people can trust and use without hand-holding • Identify and eliminate manual, repetitive processes across the BD&I team and GTM stakeholders • Build automated workflows in Salesforce, Looker Studio, and adjacent tools • Document automation logic and maintain operational runbooks for key recurring processes • Own complex and ambiguous ad hoc requests • Translate business questions into clear analytical frameworks and communicate findings clearly — in writing, in dashboards, and in live settings • Serve as the escalation point for data quality and SFDC issues — investigate root causes and drive resolution • Establish and enforce data standards across reporting domains you own • Ensure GTM KPI definitions are consistent and documented

Role Description We’re looking for a Senior Data Analyst to join our Business Analytics Engineering team. In this role, you’ll be working with cutting-edge online advertising technology and delivering top-notch data transformations, analytics, and visualizations. Our advertising spans billions of requests across thousands of dimension attributes handled by dozens of applications. Understanding the flow of data and integration points will be crucial to building representations ranging from high-level summary insights to low-level minutiae. - Become an expert on MNTN platforms, UI, databases, data pipelines, and advertising ecosystem to enable thoughtful problem-solving with a well-rounded perspective on data sources, system dependencies, and business outcomes. - Data Modeling: Apply a deep understanding of data structures and flow to design scalable data models that translate complex business and product logic into efficient code, resulting in clean and accurate tables that support both analytical and operational reporting. - Reporting & Analytics: Conduct regular data analysis and present insightful reports to internal stakeholders. Deliver high-quality, repeatable dashboards and analytics solutions. - Incident Investigation: Investigate, triage, and debug data incidents to find root causes and propose prompt, effective resolutions. - Cross-Functional Collaboration: Work closely with teams throughout the company to translate data findings into actionable insights and recommendations. Qualifications - 5+ years experience in data analytics or similar. - 2+ years experience in marketing or product analytics. - 2+ years experience in Tableau or a similar dashboarding tool. - Experienced with high volume (100M+ records) environments or data warehousing. - Extensive SQL experience with the ability to write scalable, maintainable queries. - Working knowledge of database definition language to create and manage your own datasets. - Working knowledge of ETL concepts. - Advanced Excel/spreadsheet and analytical skills. - Demonstrated ability to create data visualizations for use in communication with others through reports and dashboards. - A self-starter attitude and ability to work in a fast-paced but fun environment. - Ability to multi-task, be extremely detail-oriented, and thrive in an entrepreneurial environment characterized by growth and change. - Ability to manage multiple stakeholders and multiple priorities. Requirements - Bonus: Project management and organizational skills. - Bonus: 2+ years experience in martech or adtech with a demonstrable understanding of ad buying, attribution, and analytics. Benefits - 100% remote within the US - Flexible vacation policy - Annual vacation allowance for travel related expenses - Three-day weekend every month of the year - Competitive compensation - 100% healthcare coverage - 401k plan - Flexible Spending Account (FSA) for dependent, medical, and dental care - Access to coaching, therapy, and professional development

Role Description SafeGraph is a Data as a Service (DaaS) company with one focus: curating the most accurate, precise, and fresh points of interest (POI) database on the planet. We provide product builders, data scientists, and analytics teams with the location data they need to power site selection, transaction enrichment, advertising audiences, competitive intelligence, and more. You will play a critical role in shaping how our data products are built, validated, and trusted. This role combines product thinking with hands-on technical execution - you'll drive AI-powered product initiatives while owning the data quality processes our customers depend on. You'll sit on our Product team and work closely with Customer Success, Sales, and Engineering to identify data integrity risks early, accelerate investigations, and ship better data. Key Responsibilities - Manage initiatives to improve data product offering; work with engineering or use AI tools to define problems, shape solutions, and ship. - Investigate data quality issues independently and own the full feedback loop, from root cause to resolution to clear communication with Customer Success and customers (joining customer calls, providing written resolution notes, etc). - Design QA procedures to proactively identify and prevent data integrity issues. - Build repeatable, automated checks to reduce reliance on manual investigation. Qualifications - 2-4 years in a technical product management or analytics role. - Strong expertise in data analysis (Python, SQL). - Experience in QA for datasets. - A bias for automating repetitive tasks and reproducibility. - Experience using AI tooling to accelerate data analysis, investigation, and documentation. - Professional-level English proficiency, as English is the primary working language across the company (documentation, Slack, meetings). Nice to Have - Experience owning or contributing to AI/ML-adjacent product initiatives. - Experience with large geospatial datasets. - Experience communicating directly with customers. What Success Looks Like - Robust QA processes set up. - AI product initiatives shipping without needing close management. - Data quality issues resolve faster, with fewer escalations to engineering and clear communication to CS and customers.