Role Description We are looking for a highly skilled Senior Data Engineer to become part of our core Data & AI Engineering team. In this pivotal role, you will be responsible for designing and expanding enterprise-level data infrastructure that enables ZoomInfo's internal teams to interact with data comprehensively—extracting, exploring, analyzing, and generating insights—through various platforms using ZI's internal chat agent. The ideal candidate has a strong background in big data processing, pipeline orchestration, and data modeling, with a proven track record of delivering scalable and high-quality data solutions in fast-paced, data-centric product environments. Given the dynamic nature of emerging technologies, this role requires an individual who excels at exploration and embraces continuous learning as core responsibilities. You'll constantly research and implement innovative solutions while integrating vast, diverse data sources into our AI applications, including our industry-leading LLM-powered systems. What you’ll do: - Design, develop, and maintain high-performance, product-centric data pipelines using Airflow, DBT, and Python. - Architect and optimize the massive-scale data warehouse and lakehouse that serves as our single source of truth for all customer data, primarily using Snowflake. - Lead the integration of diverse structured and unstructured data sources (e.g., web data, third-party APIs) into our data ecosystem, ensuring high-quality and reliable ingestion. - Implement and enforce Model Context Protocol (MCP) or similar architectures to feed accurate and contextual data into our LLM-powered products for applications like Retrieval Augmented Generation (RAG) and advanced search. - Collaborate with ML engineers, data scientists, and product managers to translate business needs into scalable data solutions that directly enhance customer value. - Define, monitor, and enforce data quality SLAs across all pipelines and products, ensuring data accuracy and lineage are a top priority. - Mentor and coach junior engineers, promoting best practices in code quality, data architecture, and operational excellence. - Participate in architectural decisions and long-term strategy planning for our enterprise-wide data infrastructure, with a focus on cost, performance, and reliability. Qualifications - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - 8+ years of progressive experience in data engineering, with a track record of leadership and impact. - Demonstrated experience in implementing or scaling data infrastructure for a data-centric product company. Requirements - Expert-level SQL for building performant, scalable queries and transformations on massive datasets. - Strong Python programming skills with a focus on distributed computing, data manipulation, and building robust APIs. - Production-level experience for large-scale batch and streaming data processing. - Hands-on experience with DBT (Data Build Tool) for advanced data modeling and transformations in a modern data stack. - Deep knowledge of Snowflake data warehouse design, optimization, and cost modeling. - Experience implementing Model Context Protocol (MCP) or similar architectures to feed structured and unstructured data into LLM-powered systems. - Strong understanding of data architecture concepts, including data lakes, event-driven architectures (e.g., Kafka), ETL/ELT, and data mesh. - Proficiency with cloud platforms (GCP and/or AWS) and infrastructure as code (e.g., Terraform). Benefits - Excellent communication skills – ability to explain complex technical concepts to both engineering teams and non-technical stakeholders. - Strategic & Product-Oriented Thinking – can translate business objectives and customer needs into scalable, high-impact data solutions. - Leadership & Mentorship – experience guiding and uplifting engineering teams to achieve their full potential. - Stakeholder Management – able to collaborate effectively across departments (Product, Engineering, Sales, Compliance). - Agility & Adaptability – thrives in ambiguous, evolving environments and can rapidly prototype and iterate on solutions. - Strong documentation habits and ability to evangelize best practices across the organization.

At Murmuration, we believe that America’s promise is shaped and reshaped by the best ideas and ideals of its communities, and the dreams of the people who believe in a better life for themselves, their families, and each other.  We help organizations build power in their communities in four key ways: we organize a network of values-aligned partners; we provide deep, data-driven insights into people, places, and perspectives; we develop tools that make organizing and engagement easy and more effective; and we offer services that strengthen our partners’ capacity to lead change in their communities. We envision an America where every community has what it needs to help people lead healthy, free, and dignified lives. We work to redesign the systems and structures we all depend on — how we learn, live, govern, and solve problems — so that they are just, equitable, resilient, and rooted in shared responsibility. By strengthening the ties that hold communities together, we aim for civic life defined by collective action and care, with effective leadership that truly represents everyone.  We are a collaborative, curious, and creative team of organizers, scientists, teachers, technologists, campaign veterans, and more who share the unwavering belief that we can use our gifts in service of transforming America — together. We’ve built our team guided by the belief that the whole is greater than the sum of its parts. And so we support each other relentlessly — rallying together to face challenges the same way we celebrate each other’s wins. About the Position You're a mission-driven data engineer with a track record of building infrastructure that delivers real impact. You're deeply curious not just about how systems work but about the messy, real-world data they must support. You've done this before: you understand what "good" looks like, you deliver high quality solutions, and you help the teams around you operate more effectively. At Murmuration, you'll work with voter files from 50 states (each with its own format and quirks), census data, polling results, geographic boundaries, election returns, and more. These data sources arrive at different cadences, anywhere from multiple times a day during early voting to once a year from the census. Your role is to bring order to this complexity by building the pipelines, data contracts, and governance that transform these disparate inputs into Atlas, our unified representation of American civic life. This role is for someone who's energized by complex data problems, comfortable with ambiguity, and motivated by the understanding that strong infrastructure and data foundations is a core part of what makes research, product development, and meaningful civic impact possible. You'll partner closely with data scientists, researchers, and product teams who rely on what you build. You also stay curious about emergent technologies, particularly AI, and apply them thoughtfully to amplify your impact and the effectiveness of the broader team. Job Level P4 What You'll Do - Own data pipelines and infrastructure: Design, implement, and evolve scalable, production-grade systems using tools such as Dagster, Airflow, Snowflake, AWS, MongoDB, and dbt. Apply a cloud-native and DevOps mindset using CI/CD, infrastructure-as-code, monitoring, and automated testing to build reliable systems. Partner with cross-functional teams to deliver solutions that meet both immediate product needs and long-term organizational strategy. - Lead data ingestion and integration: Bring in complex, high-volume datasets while ensuring strong data contracts, freshness, quality, integrity, and lineage, and build systems that empower domain experts to contribute to and maintain their own data pipelines. - Transform raw data into trusted data products: Convert raw inputs into structured, usable datasets  that empower our analytical and product teams. Collaborate closely with operational data managers to ensure data models and intuitive, reliable alignment with how data is consumed in practice. - Leverage AI: Make informed judgement calls about how AI can be a force-multiplier for both your own work and the team’s and how it can’t. - Elevate the team: Mentor engineers, actively shape technical direction through architectural reviews and roadmap planning, and build team culture through documentation and knowledge sharing.

• Lead and develop robust data pipelines using Databricks, Azure Data Factory, Python and PySpark to support large-scale data projects. • Design and implement scalable solutions for integrating and processing data from multiple sources, including transactional and operational data, ensuring high data quality and governance. • Work with SQL and Databricks to extract, transform and prepare data, as well as create complex transformations to meet analytical and operational needs. • Collaborate with business and data teams, helping translate technical requirements into effective solutions that directly impact logistics operations. • Develop and automate data ingestion, transformation and analysis processes using tools such as Azure Data Factory, APIs, Kafka and Oracle GoldenGate. • Lead the optimization of data pipelines and promote high-performance data engineering practices, with a focus on performance, scalability and proactive monitoring. • Ensure data quality and integrity through testing, validation and governance, collaborating with BI and data science teams. • Mentor junior data analysts and data engineers, fostering team growth and the adoption of data engineering best practices. • Foster innovation in the use of emerging technologies, such as machine learning and artificial intelligence, to apply predictive models and automation.

• Lead teams of data engineers and analytics professionals, providing technical direction and mentorship across the project lifecycle • Architect and implement cloud-native data platforms on AWS, designing schemas and ingestion pipelines that handle structured data from diverse sources including Excel feeds, legacy databases, and third-party systems • Design, develop, and maintain ETL/ELT pipelines using AWS Glue and Step Functions, ensuring reliable data transformation, validation, and quality enforcement at every stage • Own the infrastructure-as-code practice using Terraform — provisioning, versioning, and managing all cloud resources with full reproducibility and environment consistency • Deliver end-to-end reporting solutions through Power BI, translating complex data models into intuitive dashboards that provide stakeholders with actionable, near-real-time insights • Translate complex technical concepts and architectural decisions to non-technical stakeholders clearly and persuasively, facilitating informed decision-making at all levels • Establish and enforce best practices around data quality, pipeline monitoring, documentation, and incident response • Participate in pre-sales and discovery efforts, leveraging technical expertise to support scoping, estimation, and business development initiatives