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.
The Stack
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.
What You'll Do
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- Kubernetes for data workloads or equivalent container orchestration.
- Table/lakehouse formats (Iceberg, Delta, Hudi) and judgment on where they fit against a warehouse.
- Data-quality frameworks (Great Expectations, Soda) and production observability (Prometheus, Grafana, Datadog).
- Schema registries and event contracts (Avro, Protobuf, JSON Schema).
- Catalog and discovery tooling (Microsoft Purview, DataHub, Collibra, or similar).
- GitOps tooling (Flux, ArgoCD) and FinOps practices.
- Practical data governance: PII identification, lineage, encryption, and a sane access model for regulated data.
- Familiarity with MCPs (Model Context Protocol) and exposing data to AI systems safely.
- Background in fast-paced, high-growth environments with hard delivery deadlines.
Technical Skills
Cloud & Infrastructure
- 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
Data Platform & Streaming
- 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
Governance & Security
- PII classification/labeling, lineage, change management, data-access model, encryption for regulated client data
- Secret management: Azure Key Vault or similar
Programming & Observability
- 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.
Remote USA Remote (Global) Full-time USD 170,000 – 235,000 / year
Doma is hiring a Principal Data Platform Engineer
About company
At Doma, we are transforming the mortgage closing process using cutting-edge, AI-first technology developed over years of innovation. Our mission is to dramatically reduce the cost and complexity involved with completing what has historically been one of life’s most confusing and expensive experiences. Our customers are lenders and homeowners, and our partners include the largest technology platforms that power the nation’s mortgage infrastructure.
Doma was founded in 2016 by a group of technology operators who were frustrated by the high costs that homeowners shouldered when having to complete mortgage refinancings. From a small initial team working in a shared conference room, the company grew to become one of the largest, most well-known technology companies re-shaping mortgage technology. As the company grew and refined its focus over the years, we’ve been fortunate to work with most of the top 10 mortgage lenders in the country, closely partner with Fannie Mae on introducing cutting-edge title alternative programs to the market, and (most importantly) save homeowners tens of millions of dollars in out-of-pocket costs they no longer pay because of our technology. Doma went public on the NYSE in 2021, and was subsequently taken private in 2024 with Centerbridge Partners as the sponsor.
At the heart of our three core solution offerings is a proprietary technology platform, powered by extensively patented instant underwriting technology that utilizes AI to provide safe and certain risk-decisioning across virtually any type of mortgage closing. Our technology was built over nearly a decade, with more than $100 million spent on R&D, and is integrated with all of the leading mortgage technology platforms used by mortgage originators and title insurance underwriters.
We prioritize delivering exceptional service to our customers and partners. Our escrow and settlement team ensures that for every transaction where our cutting-edge technology has dramatically reduced time and cost, we also deliver an extraordinary customer experience to mortgage operations teams, mortgage brokers, and homeowners. Similarly, we are committed to supporting our partners across the mortgage technology ecosystem as true extensions of their team, to help them solve challenges in a way that benefits them as much as it does our mutual customers.