Principal Data Platform Engineer, Doma

• 8+ years in data engineering, platform engineering, SRE, or DevOps, with a track record at Staff or Principal IC level
• Proven experience building large-scale distributed data systems in production
• Deep hands-on cloud experience (Azure preferred; AWS/GCP transferable) with IaC and CI/CD (Terraform or comparable)
• Strong SQL and data modeling fundamentals across streaming and relational/warehouse patterns
• Production depth in at least one orchestration tool and one streaming/ingestion stack
• Experience designing cross-regional database replication architectures, including failover and consistency trade-offs
• Warehouse and mart modeling with a transformation/semantic layer (dbt or comparable)
• Proficiency in Python, Go, or similar for automation and tooling
• Track record of owning ambiguous, cross-team problems end to end from approach through delivery
• Technical Skills:
• Cloud IaaS: Azure (primary), AWS or GCP transferable
• IaC: Terraform or comparable; git-backed, automated delivery
• CI/CD: GitHub Actions, GitLab CI, ArgoCD, or similar
• Orchestration: Airflow, Prefect, Dagster, or similar
• Streaming & ingestion: Kafka, Flink, Airbyte, or similar
• CDC, event streaming, batch ingestion, schema registries, and event contracts
• Warehouse: Snowflake or comparable; transformation/semantic layer (dbt or comparable); mart modeling
• Catalog and table/lakehouse formats (decisions in flight)
• PostgreSQL and operational RDBMS at scale; cross-region replication and DR patterns
• PII classification/labeling, lineage, change management, data-access model, encryption for regulated client data
• Secret management: Azure Key Vault or similar
• Python, Go, or similar; SQL; YAML/JSON/HCL for IaC and pipelines
• Metrics, logging, tracing, alerting, and data-quality monitoring for pipelines and platform services
• What We Screen For Beyond the Technical Bar:
• Written-first communication - designs, ADRs, and runbooks live in the repo
• Ownership and bias to action - you drive ambiguous problems to working systems without waiting for the next step
• Influence without authority - you build consensus and can hold the line on governance when it matters
• Principled prioritization - you protect the critical path and can explain why you said no
• Stakeholder and executive communication - you translate complex technical reality into clear risk, cost, and timeline
• Mentorship - you level up the engineers around you through design reviews, pairing, and documentation
• Self-awareness and collaboration - you know your edges and partner well across application, analytics, SRE, and security

Desirable

• We're looking for a Principal Data Platform Engineer to own the data platform as we migrate to a new cloud environment and modernize how data moves from applications through streams, into a governed warehouse, and out to marts that teams can actually use
• This is a hands-on IC role reporting to the Senior Director of Platform Engineering
• You'll be accountable for data being correct, governed, discoverable, and ready for consumption from source through curated marts. You'll partner closely with application engineering, analytics, SRE, security, finance, and executive stakeholders
• We're on Azure and expanding across cloud providers. IaC and git-backed delivery are established. Orchestration, streaming/ingestion tooling, catalog, and table/storage formats are open decisions
• You'll have real influence here. Bring depth in a modern stack and the judgment to choose well for our constraints
• Design and implement end-to-end data architecture including application CDC, streaming, schema design, transformation, warehouse/mart modeling, and consumption-readiness with IaC and automation-first delivery
• Build resilient multi-region database and replication topologies with automated failover and DR
• Architect streaming pipelines with correctness, replayability, and schema evolution as first-class concerns
• Model, document, and catalog marts for downstream self-service analytics and customer-facing consumption
• Build monitoring, alerting, and data-quality observability across data platform services
• Own PII classification, lineage, access controls, and encryption in partnership with Security
• Mentor engineers and set architectural standards across data platform craft
• Treat compute and storage spend as a design input