Data Engineers (Principal and Senior roles) job opportunity at Worth AI.

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Date2025-11-13T19:01:21.607Z bot
Worth AI Data Engineers (Principal and Senior roles)
Experience: 10-years
Pattern: Full-time
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loacation Tampa, United States Of America
loacation Tampa....United States Of America

Worth AI, a leader in the computer software industry, is looking for a talented and experienced Principal Data Engineer to join their innovative team. At Worth AI, we are on a mission to revolutionize decision-making with the power of artificial intelligence while fostering an environment of collaboration, and adaptability, aiming to make a meaningful impact in the tech landscape.. Our team values include extreme ownership, one team and creating reaving fans both for our employees and customers. Worth is looking for a Senior and Principal level Data Engineers to own the company-wide data architecture and platform. Design and scale reliable batch/streaming pipelines, institute data quality and governance, and enable analytics/ML with secure, cost-efficient systems. Partner with engineering, product, analytics, and security to turn business needs into durable data products. Responsibilities What you will do: Architecture & Strategy Define end-to-end data architecture (lake/lakehouse/warehouse, batch/streaming, CDC, metadata). Set standards for schemas, contracts, orchestration, storage layers, and semantic/metrics models. Publish roadmaps, ADRs/RFCs, and “north star” target states; guide build vs. buy decisions. Platform & Pipelines Design and build scalable, observable ELT/ETL and event pipelines. Establish ingestion patterns (CDC, file, API, message bus) and schema-evolution policies. Provide self-service tooling for analysts/scientists (dbt, notebooks, catalogs, feature stores). Ensure workflow reliability (idempotency, retries, backfills, SLAs). Data Quality & Governance Define dataset SLAs/SLOs, freshness, lineage, and data certification tiers. Enforce contracts and validation tests; deploy anomaly detection and incident runbooks. Partner with governance on cataloging, PII handling, retention, and access policies. Reliability, Performance & Cost Lead capacity planning, partitioning/clustering, and query optimization. Introduce SRE-style practices for data (error budgets, postmortems). Drive FinOps for storage/compute; monitor and reduce cost per TB/query/job. Security & Compliance Implement encryption, tokenization, and row/column-level security; manage secrets and audits. Align with SOC 2 and privacy regulations (e.g., GDPR/CCPA; HIPAA if applicable). ML & Analytics Enablement Deliver versioned, documented datasets/features for BI and ML. Operationalize training/serving data flows, drift signals, and feature-store governance. Build and maintain the semantic layer and metrics consistency for experimentation/BI. Leadership & Collaboration Provide technical leadership across squads; mentor senior/staff engineers. Run design reviews and drive consensus on complex trade-offs. Translate business goals into data products with product/analytics leaders. 10+ years in data engineering (including 3+ years as staff/principal or equivalent scope). Proven leadership of company-wide data architecture and platform initiatives. Deep experience with at least one cloud (AWS) and a modern warehouse or lakehouse (e.g., Snowflake, Redshift, Databricks). Strong SQL and one programming language (Python or Scala/Java). Orchestration (Airflow/Dagster/Prefect), transformations (dbt or equivalent), and streaming (Kafka/Kinesis/PubSub). Data modeling (3NF, star, data vault) and semantic/metrics layers. Data quality testing, lineage, and observability in production environments. Security best practices: RBAC/ABAC, encryption, key management, auditability. ** All Remote Hires - will be required to travel to Orlando, Florida at least twice per year for Town Halls and team collaboration in addition to orientation in Orlando, Florida. Nice to Have Feature stores and ML data ops; experimentation frameworks. Cost optimization at scale; multi-tenant architectures. Governance tools (DataHub/Collibra/Alation), OpenLineage, and testing frameworks (Great Expectations/Deequ). Compliance exposure (SOC 2, GDPR/CCPA; HIPAA/PCI where relevant). Model features sourced from complex 3rd-party data  (KYB/KYC, credit bureaus, fraud detection APIs)

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