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Principal Data Engineer
- Doral, Florida, United States
- Doral, Florida, United States
Über
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
Requirements
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
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)
Benefits
Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance Unlimited Paid Time Off 9 paid Holidays Family Leave Work From Home Free Food & Snacks (Access to Industrious Co-working Membership!) Wellness Resources #J-18808-Ljbffr
Sprachkenntnisse
- English
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