Key skills

Distributed Data SystemsBatch and Real-Time Data PipelinesPythonData ModelingSQLCloud-Native Data InfrastructureAWS/GCPApache AirflowApache KafkaApache SparkMachine Learning PlatformsAI and LLM InfrastructureArchitectural Decision-MakingAIMachine LearningMLAgenticRedshiftKafkaAirflowGlueLeadershipMentoring

About this role

Change.org is the world’s largest platform for democracy, using technology and AI to give people greater voice and drive social impact. The Staff Engineer, Data and AI Enablement will build and scale the data and AI platform, pipelines, architecture, and tooling that support trusted insights, personalization, experimentation, machine learning, and AI-enabled experiences. The role also provides technical leadership, operational ownership, mentoring, and architectural guidance across the organization.

Responsibilities:

Partner with PMs to translate business and product opportunities and our shared strategic vision into scalable data and AI solutions, from early exploration through production rollout
Deliver reliable data products that support data and AI enabled features, experimentation, personalization and decision-making across the company
Build and scale batch and real-time pipelines that ingest, transform, and prepare high-quality data for reporting, machine learning training, model evaluation, feature generation, and production inference
Evolve the data and ML platform architecture across orchestration, storage, compute, streaming, and data access, using technologies such as Airflow, Kafka, Redshift, Glue, Vector DBs and other cloud native services
Improve data trust and usability by establishing strong practices for data modeling, schema evolution, data contracts, testing, lineage, privacy controls, freshness, and recoverability
Enable teams to work more independently by creating reusable tools, standards, and paved paths that make it easier to discover data and build dependable workflows
Maintain a resilient and efficient platform through observability, alerting, runbooks, incident response, on-call participation, performance tuning, and ongoing cost optimization
Raise the technical bar for data and AI infrastructure by leading architectural decisions, mentoring engineers, reviewing designs and code, reducing technical debt, and advancing the use of AI and agentic workflows
This job is expected to participate in our on call rotation