Data Engineer II

Remote from: USA
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 20 Jul 2025
Apply before: 20 Aug 2025
Experience level: Midweight
Views / Applies: 12345 / 123

Pioneering health and wealth solutions as a privately owned leader.

Financial Services
2000

Actively Hiring

We are seeking a talented Data Engineer to join our growing team. This role is responsible for collaborating with an experienced team of engineers and business stakeholders to create scalable data platform solutions to support both our internal business analytics and the customer facing reporting functionality of our application. As a mid-level Data Engineer, you will take ownership of key components in our data ecosystem. Additionally, we value individuals who are team players, possess a keen eye for detail, and prioritize outcomes over outputs.

Our engineering teams are built on the principle of humans over code. We are a tight-knit group of lifelong learners in a constant quest to be a team that is greater than the sum of its parts. Come join us!

Duties & Responsibilities:

Contribute to dimensional modeling and the development of scalable data products.
Perform data transformations using SQL and Python based data frameworks
Developing and maintaining batch and streaming ETL pipelines.
Write and optimize SQL queries for internal analytics and reporting.
Create and maintain Looker explores and business reports.
Write and maintain infrastructure as code using Terraform.
Document data flows, Entity-Relationship Diagrams, and contribute to our data catalog.
Improve data quality, reliability, and observability in our pipelines and datasets.
Participate in peer-reviews of solution designs and code reviews.

Requirements:

Proficiency with SQL, Python, experience with TypeScript or Node.js is a plus.
Solid understanding of cloud data warehousing concepts (preferably in Google Cloud).
Experience setting up ETL pipelines, and orchestration tools.
Good understanding of data lakes.
Familiarity with data governance concepts.
Strong problem-solving and communication skills.
Eagerness to learn and grow in a fast-paced data environment.
Exposure to data privacy and compliance requirements is nice to have.
Knowledge of data products and data mesh architectures.
Nice to have, experience with MLOps and ML pipeline support.

Qualifications