murmuration

Content Marketing Manager

• Lead the development of integrated digital and content strategies that support organizational and partner goals;
• Conceptualize and oversee cutting-edge digital and media campaigns, including narrative strategy and creative concepts;
• Translate strategic objectives into high-impact content across platforms including video, social media, digital advertising, and email;
• Lead the development of creative concepts, messaging frameworks, ad scripts, and direct-to-audience communications (including mail where applicable);
• Own the end-to-end process for paid media and digital campaigns, from concept to execution to measurement;
• Oversee and collaborate closely with digital vendors on paid media strategy, targeting, optimization, and performance reporting;
• Establish clear KPIs and performance benchmarks, and communicate results to partners and stakeholders in clear, actionable ways;
• Use data and insights to continuously optimize creative and digital strategies;
• Manage and support the Creative Manager, providing mentorship, direction, and strategic guidance;
• Serve as a key liaison to partner organizations, translating their goals into creative and digital strategies;
• Build strong relationships with vendors, consultants, and internal teams to ensure alignment and execution excellence;
• Ensure all creative and content reflects Murmuration’s values and strengthens brand identity;
• Elevate the quality and impact of storytelling across campaigns and platforms;
• Maintain a strong understanding of emerging digital trends, platforms, and technologies to keep campaigns innovative and relevant.

Senior Manager, Content and Communications Strategy

Data Scientist

• Lead and execute data science projects, including predictive modeling, algorithm design, and data pipelines;
• Develop innovative, industry-leading campaign data science models, specifically partisanship and turnout models;
• Independently (and often collaboratively) explore data sets for insights and understanding what can help drive product development and feature prioritization;
• Transform data into actionable and compelling insights through analysis and visualization, and then effectively communicate these data-driven stories to internal and external stakeholders through memos and presentations;
• Collaborate with the Engineering Team to take data science products and concepts into production;
• Provide guidance and mentoring to level-up junior data scientists in both formal and informal learning sessions;
• Apply emerging AI technologies to R&D challenges; you’ll prototype novel approaches, evaluate feasibility, and bridge the gap between cutting-edge capabilities and practical usability.

Senior Data Scientist

Affiliate Manager

• Steward the Help Center: Create, maintain, and continuously improve knowledge articles in the Help Center. Establish and execute a regular content review cycle to ensure all articles remain accurate, relevant, and easy to use.
• Develop training content: Prepare written materials for partner and Partnership Success Manager (PSM) trainings. Collaborate closely with the Manager, Partner and User Training to ensure content aligned with partner needs. Help translate technical words and work into easy to use and understand content.
• Write and edit video scripts: Craft clear, engaging scripts for training and product videos. Edit scripts produced by others to ensure alignment with voice, accuracy, and format standards.
• Build and enforce a style guide: Develop a comprehensive style guide for all Murmuration-produced content. Own ongoing enforcement of style standards to ensure consistency and quality across every content type.
• Review and QA all training content: Serve as a quality control resource across content produced by the team, reviewing for clarity, accuracy, consistency, and adherence to style guidelines.
• Improve knowledge operations: Proactively identify gaps, redundancies, or inefficiencies in how knowledge is organized and surfaced. Recommend and implement improvements to tools, workflows, and content architecture.

Partner Education Specialist

Customer Success Manager

• Establish and manage the operational processes to identify, evaluate, and source datasets that support Murmuration’s civic engagement partners across organizing, advocacy, and electoral campaigns;
• Create and perform validation and quality checks on newly acquired datasets, including schema review, field-level analysis, completeness checks, and anomaly detection;
• Clean, standardize, and prepare external datasets for integration into Murmuration systems using SQL and scripting tools;
• Collaborate with internal teams (i.e., Data Success, Partner Success, and Research) to understand data gaps and recommend appropriate sourcing strategies;
• Monitor ongoing data quality for sourced datasets and proactively flag degradation, inconsistencies, or compliance concerns;
• Establish, implement, and share internal processes for dataset evaluation, vendor onboarding, and quality assurance;
• As new datasets are collected, develop and maintain the necessary documentation and training for internal teams on how to use and understand these datasets;
• When responsibilities allow, taking an active role in partners' data success by supporting their campaign activities and work in the Organizer platform.

Data Success Manager

SDET

• Rapidly develop a comprehensive understanding of our entire data platform (its architecture, data flows, business logic, and system interdependencies) to serve as the foundation for a testing strategy;
• Work with our application QA Engineering Team to understand our existing testing strategy and tools; this will help you identify how you can fill the gaps in data testing;
• Design, architect, and lead the implementation of an integration testing framework for the Organizer application's streaming data platform, ensuring it is complementary to existing infrastructure;
• Design automated validations that query and compare data across multiple storage systems (e.g., MongoDB, DynamoDB, Pinot) to ensure end-to-end data correctness;
• Embed automated tests within CI/CD pipelines to establish regression baselines and continuously validate new feature development;
• Design tests that rigorously account for eventual consistency, asynchronous processing, and time-based behaviors inherent to distributed systems;
• Define and own the long-term quality and testing strategy for the data platform, aligning it with organizational reliability, observability, and engineering excellence goals;
• Evaluate, select, and integrate tools, frameworks, and technologies to advance automation and data validation capabilities across the organization;
• Implement automated performance and throughput benchmarks for critical pipeline components and own the standards by which results are evaluated;
• Participate in engineering design reviews with a quality-first lens, ensuring testability, observability, and data correctness are built into systems from the start;
• Proactively identify, investigate, and drive resolution of data quality issues and systemic reliability risks; collaborate with data engineers to implement fixes and preventative measures;
• Lead post-incident reviews for data quality events: authoring clear timelines, facilitating root cause analysis, and driving follow-through on remediation action items;
• Mentor engineers across the data platform and QA teams, establishing testing best practices and elevating the team's overall quality culture;
• Represent the data platform's quality posture in cross-functional technical discussions and influence engineering standards across teams.

Staff SDET

Project Manager

• Serve as the primary project manager for multiple, high-stakes campaigns, ensuring seamless coordination, precise execution, and transparent communication across all internal and external stakeholders;
• Design, implement, and rigorously manage detailed campaign plans and timelines. This includes developing clear meeting agendas, capturing comprehensive notes, and executing proactive follow-up to ensure all campaigns and engagements are driven to successful, on-time completion;
• Serve as the key operational contact for all external partners, expertly managing inbound inquiries and communication flow;
• Provide strategic, trusted support and counsel to a diverse set of stakeholders—from grassroots student and family advocates to C-suite and senior organizational leaders—to maximize campaign effectiveness and partnership health;
• Establish and maintain project management tools and templates to drive organizational efficiency and consistency;
• Proactively identify, analyze, and mitigate potential risks and roadblocks that could jeopardize campaign timelines or outcomes;
• Track, measure, and report on overall project health, presenting regular status updates to leadership;
• Facilitate cross-functional retrospectives and post-campaign analysis to capture lessons learned and continuously improve future campaign strategy and execution;
• Manage budget tracking and vendor contracts.

Senior Project Manager, Campaigns

Manager

• Manage, coach, and develop multiple Data Success Managers, supporting strong performance, workload planning, and professional growth.
• Partner with the Sr. Manager, Data Success Management, to help manage the broader team, shape priorities, strengthen operating norms, and improve how the team works.
• Directly support a small number of partners, delivering high-quality service, translating partner needs into clear requirements, and ensuring strong outcomes.
• Oversee the day-to-day execution of core data operations, including data intake, research, acquisition, validation, issue resolution, and delivery.
• Ensure partner and vendor data is accurate, reliable, well-documented, and fit for use across Murmuration’s products, internal systems, and partner-facing work.
• Identify opportunities to improve efficiency, consistency, scalability, and quality across team workflows, and implement process improvements that refine how we support partners.
• Serve as a technical resource for the team by using SQL, Python, and strong data management practices to guide execution, troubleshoot issues, and improve workflows.
• Manage priorities and backlogs, balancing delivery timelines, data quality, partner needs, and evolving organizational priorities.
• Communicate clearly about status, risks, dependencies, and outcomes, while fostering an inclusive, collaborative team environment.

Manager, Data Success Management

Support Engineer

• Build and design data acquisition workflows. Design, build, and maintain Python and LLM-based workflows for civic and public-sector datasets, including API integrations, data parsing, transformation, and validation. Use SQL to investigate, validate, reconcile, and analyze datasets across multiple sources.
• Lead projects end-to-end. Own data acquisition work from requirements and source evaluation through implementation, monitoring, and ongoing improvement. Work with the team and cross-functional partners to design systems supporting new data types and evolving organizational needs.
• Improve continuously. Identify and prioritize opportunities to enhance data quality, efficiency, and reliability through better processes or new approaches. This includes deeply understanding the data to help design better systems.
• Get Creative. Create and leverage AI-enabled tools to accelerate development and automate repetitive tasks. Make informed judgement calls about where these tools help and where they don't. Not everything is automatable, when it’s not, you’ll roll-up your sleeves and do it yourself.
• Elevate the team. Document data sources, processes, and assumptions to support long-term maintainability. Actively collaborate with teammates across planning, brainstorming, and technical implementation — whether that's pressure-testing an approach together, pairing on a tricky pipeline, or helping a colleague think through a problem from scratch.

Senior Technical Analyst

• Build data acquisition workflows. Design, build, and maintain Python and LLM-based workflows for civic and public-sector datasets, including API integrations, data parsing, transformation, and validation. Use SQL to investigate, validate, reconcile, and analyze datasets across multiple sources.
• Implement collection end-to-end. Tackle data acquisition work from requirements and source evaluation through implementation, monitoring, and ongoing improvement. Work with the team and cross-functional partners to design systems supporting new data types and evolving organizational needs.
• Improve continuously. Identify and prioritize opportunities to enhance data quality, efficiency, and reliability through better processes or new approaches. This includes deeply understanding the data to help design better systems.
• Get Creative. Create and leverage AI-enabled tools to accelerate development and automate repetitive tasks. Make informed judgement calls about where these tools help and where they don't.
• Do the work that makes everything else possible. Not everything is automatable and not every dataset comes through an API. You'll track down information through web searches, government portals, and direct outreach, including calls to offices like secretaries of state, and wrangle raw files (PDFs, shapefiles, and worse) into usable formats. This kind of careful, methodical work is foundational to the team's output.
• Elevate the team. Document data sources, processes, and assumptions to support long-term maintainability. Support teammates through code reviews and informal technical guidance.