Principal Data Architect

Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. We deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empower s government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage . The actionable insights we deliver safeguard lives and protect critical assets around the world . Babel Street is headquartered in Reston, Virginia , with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K. For more information, visit www. babelstreet.com . Role summary Principal Data Architect, Data Platform and Pipelines We are hiring a hands-on architect to redesign and rebuild Babel Street's data infrastructure and pipelines. You will set the target architecture, write the critical code yourself, and lead engineers through a multi-year move of a petabyte-scale platform onto Google Cloud. Babel Street collects, enriches and resolves open-source and commercial data across hundreds of sources and dozens of languages. Today that data flows through pipelines and stores that grew up separately: a mix of warehouse, search and object storage, plus legacy delivery systems that are hard to change and expensive to run. Your job is to replace that with one coherent platform that is cheaper, more reliable, and built for AI and agentic workloads. This is a principal engineering role, not a people-management role. You will be judged on the systems you design and ship, the quality of your technical decisions, and how much you raise the bar for the engineers around you. You will report to the SVP of Engineering and work alongside our Head of Architecture, AI, Graph and Product teams. What you will do in your first year In your first 90 days, map the current estate end to end: every source, pipeline, store, consumer and delivery path, with its volume, cost, owner and failure history. Publish a target-state data architecture and a sequenced migration plan that engineering and product leadership sign off. Design and build the new ingestion and processing backbone. Replace bespoke pipelines with a standard pattern for streaming and batch ingestion, enrichment, and delivery, running at millions of documents per day with clear SLOs. Lead the data platform workstream of our GCP migration. Turn "lift and shift" into real modernization: decide what moves as is, what gets rebuilt, and what gets retired, and own the technical design of each wave. Rationalize storage and search. Define when we use the warehouse (BigQuery), search and index (Elasticsearch/OpenSearch), object storage, vector and graph stores, then consolidate toward that pattern and cut the cost of our most expensive workloads. Build the data foundation for AI. Deliver agent-callable data services, hybrid retrieval (keyword, analytical, vector, graph), and provenance and lineage on every record so our AI products can cite their evidence. Build the governance and compliance platform . Centralize and serve governed and compliant data (e.g. privacy laws) as part of AI-readiness, setting parameters, exclusions, and retention for optimal performance of AI. Make the platform operable. Put in place data contracts, quality checks, observability and cost attribution so every pipeline has an owner, an SLO and a known unit cost. What you will own Pipelines and ingestion. Architecture and reference implementations for collection, ingestion, parsing, enrichment (translation, NER, entity resolution) and delivery. Event-driven and queue-based processing, idempotency, backfills and replay, schema evolution, and exactly-once or effectively-once guarantees where they matter. Storage, compute and search. The physical data platform across warehouse, lakehouse, search and object storage. Partitioning, indexing, tiering and retention strategies at petabyte scale. Query and aggregation performance, and the hardware and cost trade-offs behind them. Cloud migration and environments. Technical design for moving data workloads from AWS to GCP with minimal disruption to customers. Patterns that also work in FedRAMP, disconnected and air-gapped deployments, where managed cloud services may not be available. Retrieval and AI enablement.</span