Staff Software Engineer

Who you are

• 10+ years building and operating production data infrastructure, with ownership of systems other teams depend on
• Deep experience with cloud data warehouses — Snowflake strongly preferred (BigQuery, Databricks, or Redshift experience transfers well) — including performance tuning and cost management
• Hands-on experience building CDC and streaming pipelines with technologies like Kafka, Debezium, Flink, or Spark Streaming
• Experience with managed ingestion tooling (Fivetran, Airbyte, or similar) and clear judgment about when to buy the connector and when to build it
• Strong fluency with workflow orchestration — Temporal, Airflow, Dagster, or similar — operated at scale, not just configured
• Strong programming skills in Python and advanced SQL
• Experience building frameworks or internal tooling that other engineers use, and the product instinct to know when an abstraction is helping versus getting in the way
• Practical experience with data quality, observability, and lineage tooling, and with schema evolution in systems that can't afford downtime
• Working knowledge of data governance in a regulated environment: PII classification, masking, access control, retention, and data residency
• Familiarity with cloud data services (Azure, AWS, GCP), Kubernetes, and infrastructure-as-code (Terraform, Pulumi)
• Comfort operating in ambiguity and defining scope where none exists
• Experience with dbt and a close working relationship with analytics engineering teams
• Experience with lakehouse architectures and open table formats (Iceberg, Delta Lake) or query engines like Trino
• Experience operating multi-tenant platforms with strict security, compliance, or data residency requirements
• Exposure to data infrastructure for AI products
• Prior experience as an early or founding data platform hire at a fast-growing company

What the job involves

• Own the data platform's architecture and technical direction — treating data infrastructure as a software product built from reusable frameworks, and making deliberate build-vs-buy tradeoffs as the platform grows
• Build and operate the ingestion layer across streaming, batch, CDC, and third-party connectors, including schema evolution that absorbs upstream change safely rather than silently breaking consumers, so onboarding a new source is a paved path instead of a project
• Land data into Snowflake with the freshness, completeness, and cost characteristics downstream consumers can plan around, and define a clean handoff for Analytics Engineering
• Own the orchestration platform — scheduling, retries, backfills, and dependency management across the full data graph
• Build the transformation and compute frameworks teams can use to process data at scale, and the self-serve tooling that lets product engineers and analysts stand up their own pipelines against primitives you've already made safe
• Design and operate stream processing infrastructure for use cases that can't wait for batch — real-time product features, operational alerting, and near-live reporting
• Build the trust layer: quality and observability (freshness, validation, reconciliation, anomaly detection, alerting routed to the right owner) alongside lineage, cataloging, and discovery, so anyone can find data and know where it came from and what depends on it
• Build the patterns and tooling for PII and sensitive data — classification, masking, retention, access control — and for multi-region residency requirements
• Set the technical bar for data at Harvey through design reviews, standards, documentation, and mentorship as the team grows