Role Description Looking for Philippines-based candidates. Job Role: Analytics Engineer Compensation range: $2,000 AUD - $3,000 AUD / Monthly Engagement type: Independent Contractor Agreement Work Schedule: This role is expected to align with the AU business hours (approx. 9 AM - 5 PM, Monday to Friday) for collaboration, but as a contractor, you’ll have flexibility in how you manage your time. Role Overview: - The analytics engineer builds the Gold layer and dbt Semantic Layer for each dataset phase. - Translates the metrics register definitions into governed, tested Gold models and MetricFlow metric definitions. - Works closely with business stakeholders during metric validation sessions. - Explains metric calculation choices in plain English. - Takes on support with AI monitoring responsibilities in steady state. - Comfortable with evolution in responsibilities, ideally with exposure to LLM API integration or ML monitoring. Qualifications - Advanced experience with dbt Semantic Layer and MetricFlow. - Advanced SQL expertise. - Basic Python scripting skills for automation and monitoring tasks. - Intermediate experience with Power BI / MS Fabric (or similar BI tools). - Intermediate experience with Atlan or similar data catalog tools. - Strong retail domain knowledge. - Strong communication and stakeholder management skills. - AI Tooling & Data Platform Experience. Requirements - Build Gold layer dbt models, including metric-level aggregations, KPI tables, and denormalized summary tables optimized for BI consumption in Sigma. - Author dbt Semantic Layer metric definitions using MetricFlow. - Populate and maintain the business glossary for each completed dataset. - Participate in business validation sessions with the onshore team and business stakeholders. - Author and maintain dbt Explorer documentation. - Support ongoing Run operations by managing metric change requests. - Collaborate with stakeholders and cross-functional teams. Benefits - This is a remote role that will be set up as an independent contractor engagement. - Successful candidates will be expected to disclose any existing ongoing roles or client work. - Reflect this engagement on their LinkedIn profile (clearly marked as “Independent Contractor”).

Role Description This position is needed to advance the quality, reliability, and strategic value of Twilio’s Go-To-Market (GTM) data by designing and maintaining a robust business data layer to enable trusted sales analytics, reporting, data science, & AI capabilities. The Staff, Analytics Engineer will play a mission-critical role in driving cross-functional alignment within GTM Data Science & Analytics and Sales Operations on key Sales & Finance business metrics, ensuring data consistency and empowering data-driven decision-making. Responsibilities - Design and implement a formal business data layer in dbt, ensuring that data from multiple sources is centralized, reconciled, and serves as a single source of truth for GTM metrics. - Collaborate with stakeholders within the GTM Operations team and Finance to define, document, and maintain alignment on key business metrics and data definitions. - Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards. - Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review. - Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences. Qualifications - Minimum 8 years of professional experience in analytics engineering, data engineering, business intelligence, or a related discipline, with a preference for direct experience supporting GTM or sales analytics in a SaaS environment. - Advanced expertise in SQL and hands-on experience designing data models and orchestrating data pipelines using dbt. - Demonstrated ability to partner with cross-functional stakeholders to codify, document, and reconcile critical business metrics, ensuring company-wide data alignment. - Proven track record of implementing robust data quality and testing frameworks, including expertise with dbt tests, CI/CD, and data observability. - Strong technical communication and mentorship skills, with the ability to convey complex concepts to a range of audiences. Location This role will be remote, but is not eligible to be hired in CA, CT, NJ, NY, PA, WA. Travel We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings. Benefits - Competitive pay - Generous time off - Ample parental and wellness leave - Healthcare - Retirement savings program - And much more (offerings vary by location) Compensation Please note this role is open to candidates outside of California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Vermont, Washington D.C., and Washington State. The information below is provided for candidates hired in those locations only. - Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C.: $155,520 - $194,400. - Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $164,640 - $205,800. - Based in the San Francisco Bay area, California: $182,960 - $228,700. This role may be eligible to participate in Twilio’s equity plan and corporate bonus plan. All roles are generally eligible for the following benefits: health care insurance, 401(k) retirement account, paid sick time, paid personal time off, paid parental leave. The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location. Application Deadline Information Applications for this role are intended to be accepted until July 31st 2026, but may change based on business needs. Company Description Twilio thinks big. Do you? We like to solve problems, take initiative, pitch in when needed, and are always up for trying new things. That's why we seek out colleagues who embody our values — something we call Twilio Magic. Additionally, we empower employees to build positive change in their communities by supporting their volunteering and donation efforts. So, if you're ready to unleash your full potential, do your best work, and be the best version of yourself, apply now! If this role isn't what you're looking for, please consider other open positions. Twilio is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Additionally, Twilio participates in the E-Verify program in certain locations, as required by law.

• Build scalable Python-based data pipelines and backend services for analytics workflows. • Design software systems using object-oriented programming and sound engineering practices. • Create and support platforms that allow analytics development, model training, and model deployment. • Implement and maintain CI/CD pipelines and infrastructure-as-code solutions for automated deployments. • Manage cloud and on-premises analytics environments, including AWS infrastructure and security controls. • Monitor, troubleshoot, and improve data pipelines, platform performance, and system reliability. • Support machine learning and fraud modeling workflows, including feature engineering and model deployment. • Implement new technologies, including AI-based solutions, to improve platform efficiency and stability.

• Partner with business stakeholders across Operations, Customer Success, Product, Growth, and Learning to scope analyses, define metrics, and answer the questions that drive decisions. Translate ambiguous, business-language requests into clear analytical work that holds up to scrutiny and leads to action. • Drive the analysis itself — bring judgment about what question is actually being asked, what the right analytical approach is, and what "good" looks like. Push back when a request is underspecified and shape vague asks into rigorous analyses. • Own dashboards end-to-end with a strong business-intelligence mindset — design, build, maintain, and iterate based on stakeholder feedback to make data products that people actually use. You'll work in Hex (our primary BI tool), but we're looking for BI craft and judgment, not Hex-specific experience. • Adapt your message to the audience — surfacing the action-oriented takeaway for an executive, walking through the details with a peer, and producing written analyses that hold up after you've left the meeting. • Bring a data engineering and data architecture mindset — think about what data models should exist, how data flows between source systems and the warehouse, how schemas connect, and where pipelines need to be reinforced. Partner on data modeling decisions and contribute to the warehouse (in our case, primarily through dbt). • Take ownership of data quality and trust. Build tests into the models you work on, set up monitoring on the dashboards and pipelines that matter most, and respond when something breaks. The bar is "stakeholders should be able to trust what they see." • Collaborate closely with engineering — partner with the product and engineering teams on event instrumentation, source data quality, schema changes, and anything else upstream that affects what's available for analytics downstream. Be the analytics voice in engineering conversations, and the engineering-aware voice in analytics conversations. • Help shape the long-term direction of the data team — beyond day-to-day analysis, contribute your perspective on where the data architecture and analytics practice should be heading. Weigh in on what we should be measuring, how we should be instrumenting and collecting data, what tools belong in our stack, and where we should be investing for the next year and beyond. You won't own these decisions alone, but you'll be expected to bring informed opinions and help drive them forward. • Lean into an analytics culture where AI is already central to how we work. We use modern AI tooling (Claude Code, agentic skills) heavily in our day-to-day, and we're actively building out how the data team uses it. • Explore how AI-assisted workflows can improve the analytics craft - for SQL development, dbt modeling, dashboard prototyping, exploratory analysis, documentation, and code review. Bring practical experience and curiosity rather than buzzwords. • Contribute to how the data team develops reusable patterns, skills, and guardrails for working effectively alongside AI systems. • Help raise the bar across the small data team — review each other's SQL and dbt code, share patterns, and pair on tricky analyses. Be someone teammates want to learn from. • Bring others along in your work. Make decisions and trade-offs visible so the rest of the team learns from them, not just from the outcomes. • Operate as a steadying presence when priorities shift, requirements change, or analyses don't go the way anyone expected. Help the team stay focused on the decisions the work is meant to support.