• Design, develop, and maintain scalable ETL/ELT pipelines using Python and SQL. • Ensure high data quality, integrity, and availability for downstream analytics and pharmacy workflows. • Collaborate with data scientists, analysts, and pharmacy domain experts to understand requirements. • Troubleshoot and optimize data models and pipelines for performance and reliability. • Work in a cloud environment to support modern, scalable pharmacy technology infrastructure. • Play a critical role in enabling pharmacy data flows and ensuring end-to-end software reliability.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're looking for a Data Engineer to build and scale the data infrastructure that powers Runway's AI research and business intelligence. You'll own critical data pipelines spanning production databases, analytics warehouses, and large-scale ML training datasets. This role sits at the intersection of data engineering, ML infrastructure, and analytics—you'll enable both world-class research and data-driven business decisions. You'll work on challenging problems at scale: - Managing billions of rows of multimodal training data - Building CDC streams from production systems - Optimizing vector databases for ML workflows - Creating the foundational data layer that the entire company relies on A peek at our technical stack: - LanceDB for vector storage and dataset versioning with multimodal training data - ClickHouse as our analytics warehouse receiving CDC streams from production Postgres via AWS Kinesis - BigQuery for training run logs and evaluation results - Ray for large-scale distributed data processing on managed Kubernetes clusters - dbt for standardized transformations - Prometheus and Grafana for monitoring - Terraform for infrastructure management This is an opportunity to bring best practices and technical leadership as we mature our data infrastructure to support rapidly growing ML training and research needs. Qualifications - 4+ years of industry experience in data engineering - Strong knowledge of Python - Experience with data quality, deduplication, and cleaning at scale - Comfortable working with cloud storage (S3) and managing large datasets - Experience building and maintaining ETL/CDC pipelines at scale - Strong SQL skills and experience with multiple database systems (Postgres, columnar databases like ClickHouse/Redshift) - Humility and open mindedness; at Runway we love to learn from one another Requirements - Experience with one or more frameworks for large-scale data processing (e.g. Spark, Ray, etc) - Knowledge of cloud platforms (AWS, GCP, or Azure) and their data service offerings - Knowledge of data privacy and data security best practices - Experience with business intelligence and visualization tools (e.g., Looker, Tableau, PowerBI, Metabase, or similar) - Experience in a high-growth startup environment or similar fast-paced setting Benefits - Salary range: $240,000 - $290,000 - Commitment to creating a space where employees can bring their full selves to work - Equal opportunity to succeed regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status

• Lead data migration from legacy systems to Guidewire InsuranceSuite • Collaborate with business analysts, data analysts, and development teams • Define project scope, user stories, acceptance criteria, and estimates • Analyze complex data structures and map them to Guidewire model • Manage end-to-end data loading process • Resolve data quality issues and migration errors • Enable offshore teams to deliver business requirements • Work on improving internal processes and methodologies.

• Design, build, and operate scheduled and event-driven data pipelines for simulation outputs, telemetry, logs, dashboards, and scenario metadata • Build and operate data storage systems (structured and semi-structured) optimized for scale, versioning, and replay • Support analytics, reporting, and ML workflows by exposing clean, well-documented datasets and APIs • Enable scenario comparison, lineage, and traceability across time, versions, and parameter sets • Work with engineers to define data contracts and schemas between the simulation environment and downstream systems • Monitor and improve data reliability, performance, and availability