JOBS by allUP
Overview
Join to apply for the
Principal Data Engineer
role at
JOBS by allUP .
This range is provided by JOBS by allUP. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $150,000.00/yr - $175,000.00/yr
About our client Worth AI , a leader in the computer software industry, is looking for a talented and experienced Principal Data Engineer to join its innovative team. Worth AI is 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. The team values include extreme ownership, one team, and creating raving fans both for employees and customers.
About the role Worth AI
is looking for a
Principal Data Engineer
to own the company-wide data architecture and platform. The individual will design and scale reliable batch/streaming pipelines, institute data quality and governance, and enable analytics/ML with secure, cost-efficient systems.
They will partner with engineering, product, analytics, and security to turn business needs into durable data products.
Responsibilities
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.
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.
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.
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).
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
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Software Development
Industries
Software Development and Financial Services
Referrals increase your chances of interviewing at JOBS by allUP by 2x
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr
Principal Data Engineer
role at
JOBS by allUP .
This range is provided by JOBS by allUP. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $150,000.00/yr - $175,000.00/yr
About our client Worth AI , a leader in the computer software industry, is looking for a talented and experienced Principal Data Engineer to join its innovative team. Worth AI is 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. The team values include extreme ownership, one team, and creating raving fans both for employees and customers.
About the role Worth AI
is looking for a
Principal Data Engineer
to own the company-wide data architecture and platform. The individual will design and scale reliable batch/streaming pipelines, institute data quality and governance, and enable analytics/ML with secure, cost-efficient systems.
They will partner with engineering, product, analytics, and security to turn business needs into durable data products.
Responsibilities
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.
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.
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.
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).
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
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Software Development
Industries
Software Development and Financial Services
Referrals increase your chances of interviewing at JOBS by allUP by 2x
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr