Key skills
PythonCSQLAILLMData EngineeringAnalyticsSnowflakeBigQueryRedshiftKafkadbtAWSAzureGCPPostgresMySQLSaaSLeadership
About this role
Syndio is a Series C technology company focused on leveraging advanced technology and responsible AI to enhance compensation strategies. The Staff Data Engineer will define the architecture of Syndio's data platform, focusing on data structuring, modeling, and activation to support AI-driven products and analytics.
Responsibilities:
- Define the data architecture behind AI for Pay Decisions and future decisioning systems
- Build the data foundation that powers LLM-based features, including the pipelines that feed retrieval, context generation, and evaluation workflows
- Own high-leverage problems like automated schema mapping and multi-tenant data standardization
- Establish standards for data contracts, modeling, and pipeline architecture across teams
- Design data governance into the platform through policy tagging, PII handling, and lineage tracking
- Build pipelines that support both batch analytics and the event-driven data flows behind AI workflows
- Shape how data is structured, delivered, and made reliable for stakeholders across Syndio, from analytics and product teams to the teams training and evaluating AI systems
Requirements:
- Deep experience in data engineering, data platform, or distributed systems roles
- Proven track record designing and scaling data pipelines across teams or organizations
- Experience solving ambiguous, high-impact platform problems and building systems widely adopted across teams
- Strong ability to operate at both architectural and hands-on levels
- Cross-functional leadership and ability to influence technical direction
- Data modeling and schema design
- Pipeline architecture and orchestration
- Data warehouse and storage systems
- Strong proficiency in Python and SQL
- Experience with data patterns for AI/LLM systems: retrieval-ready data modeling, vector storage, embedding pipelines, and evaluation datasets
- Cloud platforms: GCP (preferred), AWS, or Azure
- Cloud data warehouses: BigQuery, Snowflake, Redshift, or similar
- Data transformation: dbt or equivalent modern transformation frameworks
- Relational databases: Postgres, MySQL, or similar
- Streaming and CDC ingestion patterns (Datastream, Debezium, Kafka, or similar)
- Data governance and lineage tooling
- Experience building AI-native data platforms or developer tooling
- Experience with event-driven or streaming architectures
- Background in high-scale systems or large multi-tenant SaaS platforms
- Familiarity with compensation or HR data domains