Principal Data Engineer, Analytics

Jobtailor; California (MO); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor is seeking an experienced analytics engineer to own end-to-end data products in a FinTech-focused environment. You will design and optimize dbt models and Python-based processing pipelines, collaborating with stakeholders across Product, Finance, and Risk.

Expect rigorous testing, CI/CD, and scalable architectures within Databricks. Ideal candidates bring 8+ years in analytics/data engineering, expert SQL, and hands-on experience with dbt, Databricks, and AWS services.

Qualifications

• 8+ years of analytics engineering or data engineering experience.
• Expert SQL and advanced Python for data manipulation.
• Experience with Databricks, dbt materializations, and AWS services.
• Experience in FinTech/Capital Markets data modeling and reporting.
• Strong CI/CD, testing, and documentation practices.

Responsibilities

• Own full lifecycle of data products from concept to production.
• Design and code complex dbt models for high-volume datasets.
• Write production-grade Python for data processing and orchestration.
• Optimize performance and reduce cost/latency across pipelines.
• Lead data initiatives with cross-functional stakeholders.
• Mentor teammates and perform code reviews and pair programming.
• Maintain data sources, statistics, and data quality tests.

Skills

SQL

Python

Dbt

Databricks

Data Modeling

Data Quality Testing

CI/CD Implementation

Unit Testing

Performance Optimization

Analytics Engineering

Data Engineering

Education

Bachelor's degree in Computer Science or related field

Tools

Git

GitHub Copilot

Airflow

AWS

Job description

• Own the full lifecycle of data products from conceptualization and architecture through production deployment, optimization, and maintenance
• Design and code complex dbt models and transformation logic for high-volume financial datasets
• Write production-grade Python scripts for data processing, anomaly detection, and custom orchestration
• Solve query-performance problems and optimize legacy code and incremental loading strategies to reduce cost and latency
• Own implementation of data deployment and reporting pipelines using Git and dbt Cloud
• Engineer automated data-quality testing and validation frameworks using dbt tests and Python
• Lead major data initiatives, including scoping, design, project management, and hands‑on coding
• Partner with Product, Finance, Operations, Risk, and Trading stakeholders to build data products and reporting solutions
• Conduct code reviews, pair programming, mentorship, and architectural guidance
• Curate and maintain key data sources and statistics for internal teams and external partners

Requirements

• 8+ years of professional experience in analytics engineering or data engineering
• Expert proficiency in SQL
• Advanced proficiency in Python for data manipulation using Pandas, Polars, or Spark, and interaction with APIs/AWS services
• Experience using Databricks for analytics workloads, including Databricks SQL and dbt
• Experience with dbt materializations, macros, and package management
• Experience architecting data models for FinTech and Capital Markets, including trade lifecycles, clearing/settlement, risk models, and financial reporting
• Proficiency leveraging AI tools such as GitHub Copilot or similar LLMs
• Proven ability to build reusable and modular data solutions
• Experience implementing unit testing, CI/CD, comprehensive documentation, and SLA monitoring
• Ability to diagnose and resolve complex errors or issues within distributed systems such as Spark/Databricks
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field
• Applicants must be authorized to work for any employer in the U.S. without current or future visa sponsorship
• Nice‑to‑have: Experience with Airflow or similar orchestrators
• Nice‑to‑have: Experience with Sigma Computing
• Nice‑to‑have: Experience building Data Apps within Databricks
• Nice‑to‑have: Experience using AI/LLM tools for analytics workflows

Core Competencies

Demonstrates expertise in data engineering and analytics, with a strong focus on SQL, Python, and dbt for building and optimizing data products in FinTech and Capital Markets. Proven ability to lead data initiatives, implement CI/CD practices, and ensure data quality through automated testing and validation.

Highest‑signal resume keywords

• Expert Proficiency In SQL
• Advanced Proficiency In Python
• Experience Using Databricks
• Experience With Dbt Materializations
• Ability To Diagnose Complex Errors

ATS Optimization Keywords

Hard Skills

• Data Engineering
• Analytics Engineering
• SQL
• Python
• Dbt
• Data Modeling
• Data Quality Testing
• CI/CD Implementation
• Unit Testing
• Performance Optimization

Soft Skills

• Project Management
• Mentorship
• Collaboration

Industry Keywords

• FinTech
• Capital Markets
• Trade Lifecycles
• Risk Models
• Financial Reporting

Tools & Technologies

• Databricks
• Git
• GitHub Copilot
• Airflow
• AWS