Principal Data Platform Engineer

Role overview

Join Doma as a Principal Data Platform Engineer. You will own the data platform during a migration to a new cloud environment and modernize the company’s data architecture and processes. This is a hands-on role with significant impact on data strategy, quality, and how data is consumed across teams.

Key missions

• Design and implement end-to-end data architecture, including:
  • schema design
  • transformations
  • warehouse and mart modeling (with a transformation/semantic layer)
• Build resilient multi-regional databases and replication topologies with:
  • automatic failover
  • disaster recovery (DR)
• Own data quality, governance, data discoverability, and data readiness for consumption

Responsibilities

• Take ownership of ambiguous, cross-team problems from approach through delivery
• Build and operate large-scale distributed data systems in production
• Ensure strong production practices for data reliability and observability
• Mentor engineers through design reviews, pairing, and documentation
• Influence stakeholders without authority; uphold governance and risk management

Requirements

• 8+ years in data engineering, platform engineering, SRE, or DevOps; track record at Staff/Principal IC level
• Deep hands-on cloud experience, Azure preferred (AWS/GCP transferable)
• IaC and CI/CD experience (e.g., Terraform or comparable)
• Production depth in at least:
  • one orchestration tool
  • one streaming/ingestion stack
• Strong fundamentals in SQL and data modeling for both streaming and warehouse/relational patterns
• Experience with large-scale distributed systems in production
• Experience designing cross-regional replication architectures, including failover and consistency trade-offs
• Warehouse and mart modeling with a transformation/semantic layer (e.g., dbt or comparable)
• Experience with data catalog/discovery tooling (e.g., Microsoft Purview, DataHub, Collibra)
• Knowledge of schema registries and event contracts (e.g., Avro/Protobuf/JSON Schema)
• Practical data governance for regulated data (PII identification, lineage, encryption, and access model)
• Data quality and observability tooling (e.g., Great Expectations/Soda; Prometheus/Grafana/Datadog)

Nice-to-haves / additional skills

• GitOps (e.g., Flux / ArgoCD) and FinOps practices
• Lakehouse/table formats: Iceberg/Delta/Hudi
• Schema exposure to AI safely and familiarity with MCP (Model Context Protocol)
• Kubernetes for data workloads (or equivalent)
• CI/CD tools (e.g., GitHub Actions/GitLab CI/ArgoCD)
• Secret management (e.g., Azure Key Vault)
• Tech for data platforms:
  • Streaming/ingestion: Kafka/Flink/Airbyte
  • Orchestration: Airflow/Prefect/Dagster
  • Warehousing: Snowflake (or comparable)
  • Operational RDBMS at scale (e.g., PostgreSQL) with DR/replication patterns
• Event-driven and ingestion fundamentals: CDC, schema registries, and event contracts
• Strong communication, mentorship, and cross-functional collaboration

Location / work model

• Full remote (New York, US).