About Outreach
Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few.
About the Team
Data is at the core of Outreach's strategy. It drives our product and our customers to the highest levels of success. We use it to derive customer health scores and revenue dashboards, monitor operational metrics of our AWS infrastructure, increase product engagement and user productivity through natural language understanding, and power predictive insights and causal inference via experimentation. As our customer base continues to grow, we are looking towards new ways of leveraging our data to deeper understand our customers’ needs and deliver new products and features to help continuously improve their customer engagement workflows.
The Business Systems Data Engineering team is on a mission to bring relevant data to every person at Outreach while advancing the use of data and promoting an insight-centric culture. We are a centralized resource providing a suite of services to Outreach business functions across Finance, Analytics, and Support systems, with data as the critical component to a seamless experience.
Our Vision of You:
Data is your passion, from schema design to understanding data pipelines to data validation and optimization. You love everything about data and live in this world.
You strive to become an expert in analytics toolchains and solutions (such as Databricks, Python, Spark) and understand their nuances as you optimize schemas to take advantage of their unique features.
You are meticulous about data and data-pipeline quality and obsessive about building solutions that verify data correctness at every step of the pipeline
Qualifications:
7+ years of Data Engineering experience.
Support strategic planning by working with Executives and Senior Managers, Principle Engineers, and cross-team Data Engineers.
Good understanding of RDBMS and NoSQL data stores and when to use each
Experience with OLAP and OLTP type datastores including schema design and query optimization
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Experience with distributed bus systems (Kafka, RabbitMQ)
Strong skills in at least one programming language languages (Python highly preferred).
Experience with Spark Ecosystem (Delta Lake, Databricks, etc.)
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
Strong foundation in data modeling, schema design, and data quality best practices, with functional experience working on cloud platforms like Snowflake or Databricks.
Expertise in building multi-step ETL jobs through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub.
Strong communication and collaboration skills, with a focus on clarity, empathy, and shared ownership.
You embody our core values: we are hungry craftspeople, we have grit, we are honest, we take ownership, we have each other’s back no matter what, we’re one with our customers, and we find strength in diversity and inclusion