Key Facts

Remote From:

Florida (USA)

Full time

Mid-level (2-5 years)

Hard Skills

AWS Cloud Services Data Management Data Architecture Data Lineage Amazon Managed Streaming for Apache Kafka (Amazon MSK) Database Schema Encryption Event Processing Language Change Data Capture (CDC) Product Data Management Capacity Planning ETL (Extract Transform Load) Data Partitioning Technology Roadmaps SQL (Programming Language) Query Optimization Data Governance Auditing Data Quality Assessment Python (Programming Language) Java (Programming Language) Data Collection Storage Architecture Lab Notebooks Alation Data Catalog Incident Management Scala (Programming Language) Key Management Metadata Management Text Manipulation DataHub (Software) Anomaly Detection Data As A Service (DaaS) Exploratory Testing Multi-Tenant Cloud Environments Cross-Functional Collaboration Technical Leadership Stakeholder Communications Build vs Buy Analysis

Other Skills

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Accountability
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Communication
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Adaptability

Roles & Responsibilities

10+ years in data engineering with at least 3+ years in staff/principal or equivalent
Deep experience with AWS and a modern data warehouse/lakehouse (e.g., Snowflake, Redshift, Databricks)
Strong SQL and proficiency in at least one programming language (Python or Scala/Java); experience with orchestration (Airflow/Dagster/Prefect), transformations (dbt), and streaming (Kafka/Kinesis)
Data modeling (3NF, star, data vault) with semantic/metrics layers; data quality, lineage, and observability in production; security best practices (RBAC/ABAC, encryption)

Requirements:

Define end-to-end data architecture (lake/lakehouse/warehouse), batch/streaming, CDC, and metadata; set standards for schemas, contracts, orchestration, storage layers, and semantic/metrics models; publish roadmaps and guide build vs. buy decisions
Design and build scalable 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
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
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

Worth AI

Financial Services

About Worth AI

Welcome to Worth AI, where we're reshaping how decisions are made, guided by inclusivity and innovation. Join Us: Be part of a global movement that values diversity and technology's power. Worth AI offers a chance to make a meaningful impact, redefine worth, and create a more equitable world. We are on a mission to revolutionize the way businesses make decisions leveraging the power of AI. Our Mission: At the heart of Worth lies our unwavering commitment to "Equitable Decision Making." We believe that every decision, big or small, should be grounded in fairness, inclusivity, and diversity of thought. Our mission is to champion these principles in every facet of our business. Our core values define who we are and how we operate: Diversity of Thought: We are committed to creating an environment where diverse perspectives are not only welcomed but celebrated. One Team: We are more than just a group of individuals; we are one team with a shared purpose. Agility: We don't just adapt; we lead. Agility is the driving force that propels us to embrace the opportunities that come when change happens. Our Purpose: We define "Worth" as the intrinsic value within each person and organization. We are focused on making a positive impact in the business world and leaving a legacy of fairness and inclusivity. Join Our Movement: If you're passionate about making a meaningful difference, and if you're ready to redefine worth, then our company is the place for you. Come be a part of our journey as we transform industries and leave a lasting legacy of equity and excellence. Together, let's make Worth the cornerstone of a more equitable world.

Company type: Small startup

Industry: Financial Services

Founded: 2018

Company size: 2 - 10

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Job description

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 Principal Data Engineer 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.

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.

** 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)

Benefits