• Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platforms. • Build scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasets. • Implement data transformation logic to support analytics, reporting, and regulatory use cases. • Ensure data pipelines are reliable, performant, and scalable in cloud environments. • Leverage AI-assisted development tools to accelerate pipeline development, testing, and optimization. • Develop and manage data solutions leveraging AWS services (e.g., S3, Airflow, DAGs, Glue, Lambda, Redshift). • Implement and optimize Apache Iceberg table formats for large-scale, ACID-compliant data lakes. • Support lakehouse architectures that unify data lakes and data warehouses. • Optimize data storage and retrieval strategies for performance and cost efficiency. • Enable data platforms that support AI/ML workloads and downstream generative AI use cases. • Design and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using various tools. • Automate build, test, and deployment processes for ETL pipelines and data platform components. • Implement DataOps best practices, including version control, automated testing, environment promotion, and rollback strategies. • Ensure reproducibility, reliability, and governance of data pipeline deployments across environments. • Integrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational risk. • Design and implement materialized views and other performance optimization techniques to improve query efficiency. • Develop pipelines to ingest, parse, and normalize XBRL data. • Apply context engineering principles to ensure data is enriched with meaningful metadata, lineage, and business context. • Collaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutions. • Work in Agile teams to iteratively deliver data capabilities and enhancements.

• Design and implement scalable enterprise data architectures leveraging AWS and Apache ecosystem technologies (e.g., Spark, Iceberg). • Architect modern AI-enabled data platforms, including support for machine learning, LLM integration, and retrieval-augmented generation (RAG) patterns. • Develop and maintain conceptual, logical, and physical data models, including Entity Relationship Diagrams (ERDs). • Architect modern data lakehouse and data warehouse solutions using Apache Iceberg and cloud-native services. • Define and enforce standards for data integration, data quality, and data lifecycle management. • Design and implement Knowledge Graph architectures, integrating structured and unstructured data sources. • Design and implement Knowledge Graphs and semantic data layers using ontologies, taxonomies, and linked data principles. • Apply GraphRAG architectures to enhance LLM-based applications with context-aware, explainable data retrieval. • Develop and manage ontologies and semantic models to enable interoperability, data discovery, and advanced analytics. • Integrate AI/ML and generative AI capabilities into enterprise data ecosystems, including vector databases and embedding pipelines. • Ensure alignment between data architecture and AI governance, including model transparency, traceability, and responsible AI practices. • Establish and manage enterprise metadata frameworks, including data dictionaries, business glossaries, and technical metadata repositories. • Lead or support stakeholder listening campaigns to gather input from executives, data leaders, and practitioners across the enterprise. • Collaborate with stakeholders to identify data challenges, AI use cases, and opportunities for advanced analytics and automation. • Support analysis of alternatives (AoA) for data and AI tools/platforms, providing recommendations based on cost, capability, and mission fit. • Track and report on data strategy progress, maturity improvements, and program outcomes.

Role Description As the R&D powerhouse for the progressive movement, we are looking for 2 roles, one Senior and one Senior or Mid-level Data Engineer to join the Tech department. What the work looks like for our data team: - Design and implement scalable data architecture for targeting and engagement - Serve as technical partner to program teams - Extend our foundational datasets to facilitate new outreach strategies - Load-test our systems to prepare for elections - Maintain key datasets for polling images and precinct cross-matching - Develop standardized experimental frameworks for testing campaign effectiveness - Build AI tooling that makes our own team faster — QC agents for data deliveries, the evals to trust them, and context systems that keep our repos self-documenting Our data team’s work mixes greenfield builds, pragmatic plumbing, incident response, and operational work. The relative mix shifts with the political calendar. These roles will run from June 1 through November 20. These roles are fully remote. Responsibilities - Shape the pipelines and integrations feeding our RCT platform and our foundational voter datasets: the APIs, replication, federation layers, experiment randomization, and the failure modes that come with running data systems for hundreds of partner organizations on a non-negotiable political calendar - Extend our data platform and orchestration layer, and optimize it for cost, runtime, and reliability so it holds up during the elections, with millions of outreach attempts - Lead projects that span multiple systems and teams — scoping, designing, and driving them to real milestones - Hold the bar on quality — test and validate your own work, review teammates' code, and mentor through that review - Document and communicate — make your systems understandable to teammates, stakeholders, and non-engineers - Rotate through on-call and rapid response, including evenings and weekends during high-stakes moments like election cycles - Passionate about progressive politics and leveraging data, technology, organizing and research to build political power Qualifications - 3-6+ years of experience (Senior or Mid-level) in designing, building, and operating production data pipelines and integrations - Strong proficiency in SQL and Python, with hands-on experience across a modern data stack — cloud data warehouses (BigQuery or similar), a transformation framework like dbt, and an orchestrator like Dagster or Airflow - A track record of leading technical work across multiple systems or teams, and scoping ambiguous problems end-to-end - Sound judgment about when to refactor, when to ship pragmatically, and how to leave systems better than you found them - Strong written communication — you document what you build and can explain complex systems to non-engineers - Genuine commitment to the mission of stopping fascism and building progressive power, and comfort operating on a political calendar Nice to Have - Experience in the progressive, political, advocacy, or nonprofit sector — including awareness of c3/c4/PAC boundaries and the sensitivities of political and voter data - Familiarity with election results, voter file data, and the tools campaigns use to organize, text, and contact voters - Experience on Google Cloud Platform - Experience in on-call rotations and mentoring less-experienced engineers Benefits - Full year equivalent salary of $90,000–$99,000 for Data Engineer (Level 3) or $110,000-$119,000 for Senior Data Engineer (Level 5) as part of a transparent salary structure with clear levels of advancement, and an additional Cost-of-Living salary Adjustment. - Excellent health, dental, and vision benefits, 401(k) matching, and unlimited Paid Time Off (vacation time is limited during Election Sprint, August 24–November 3). - Remote-first culture with teammates across the country. - These positions are temporary and will run through November 20. We love to keep top performers if possible. Application Process Applications submitted by May 11, 2026 will be given priority. Applications may be reviewed earlier on a rolling basis so it benefits candidates to apply as soon as possible. The application process includes an initial screen, an exercise, one or two full interviews, and a reference check. Due to the nature of our work, the process is moving quickly, and we hope candidates will start by June 1, 2026. Applicants must be legally eligible to work in the United States. We are not able to provide sponsorship at this time. Equal Opportunity Employer Movement Labs is an equal opportunity employer with a commitment to economic and social justice. Women, people of color, LGBTQ people, people with disabilities, and members of other historically disenfranchised populations are strongly encouraged to apply. If you require accommodations as part of the hiring process, please contact careers@movementlabs.com.

Role Description At Clari + Salesloft, our Data Engineer will be pivotal to our company’s success. You will be a key member of our fast-growing and high-performing Data & Analytics (D&A) team, helping build and maintain the data infrastructure that powers all of our internal reporting and analytics for multiple business teams. As a team, we are actively implementing the latest AI features and cutting-edge technologies to drive business insights. On a day-to-day basis, you will be responsible for architecting and delivering end-to-end data pipelines to support internal stakeholders. Specifically, you will: - Design, build, and maintain scalable data pipelines and services for analytics and business intelligence. - Develop data flows and applications for machine learning, analytics, and third-party data integrations. - Collaborate with data scientists and analysts to deploy models and analytical solutions. - Perform heads-down development, test your code, review peer code, and ship it to production. In addition to working with amazing colleagues who exemplify our ‘team over self’ core value, you will also have the opportunity to build and enhance the data infrastructure that provides critical insights to business leaders, directly impacting strategic decisions across the company. You will have an opportunity to make a difference. Qualifications - Strong SQL skills and experience with Snowflake core features, focusing on performance optimization and efficient data warehousing practices. - Proven ability to build scalable, tested transformation layers using DBT. - Experience managing data pipelines with Airflow and ingestion tools such as Fivetran or Estuary. - Comfortable working with AI-assisted development tools such as Snowflake Cortex, Gemini Enterprise, or GitHub Copilot. - Strong Git workflow experience in Agile environments (Jira). - Bachelor’s degree in Computer Science or a related technical field. Requirements - Experience with BI/analytics tools and supporting reporting workflows. - Experience working with Salesforce or other CRM data, particularly in SaaS environments. - Familiarity with AWS or GCP, REST APIs (Postman), or Databricks. - Exposure to Machine Learning, AI, or Data Science frameworks. Benefits - Inclusive and supportive workplace where everyone belongs and can thrive. - Focus on culture add, not culture fit, enhancing team strength through diverse perspectives. - Equal Opportunity Employer providing employment opportunities without regard to protected characteristics.