Associate Client Data Engineer

Strada Education Foundation•Washington, DC

47d•$74,850 - $91,700•Hybrid

About The Position

As an Associate Client Data Engineer at CredLens, you will focus on the client-onboarding workflow that brings new credential-issuer data into our platform. You will learn the platform and the fundamentals of production data engineering with direct support from more senior engineers, and gain hands-on experience across the full onboarding workflow, with a clear path to grow into a broader data-engineering role over time. Because so much of the work involves direct client interaction, excellent customer-service skills and clear communication are essential. This is an early-career, hands-on role focused exclusively on onboarding: partnering directly with clients to receive their data, understanding and documenting the data structure, and cleaning, transforming, and validating the data so it is ready for CredLens' data pipeline. This is an excellent opportunity to start a data career from the ground level by continuing to build a data engineering skillset while working in a role that directly impacts end users. You will work within a small, cohesive Data Engineering team, reporting to the Data Engineering Team Lead. The team is based in Washington, DC. This is a hybrid role requiring in-person attendance at the office at least two days a week, likely Tuesdays and Thursdays. Note: this is not a "big data" or "real-time" analytics role. It best suits a mission-aligned individual who cares about using data to create more equitable outcomes and who focuses on quality, reliability, and a smooth client experience. CredLens is building a nonprofit national data trust focused on verified outcomes for non-degree credentials. The effort is an initiative launched by the Strada Education Foundation in 2024. CredLens will deliver actionable insights and power ongoing research for industry-based, professional, and workforce credentials. CredLens is designed to fill the data gap for non-degree credentials. The attainment of these credentials is growing, but there is little to no data tracking their outcomes. CredLens will offer tailored data analytics and visualizations to credential issuers, workforce training providers, philanthropic funding partnerships, and state system partnerships to support the continuous improvement of credential quality and to support informed funding and scaling decisions.

Requirements

Bachelor's degree in computer science, information systems, data science, or a related field is required. OR, equivalent practical experience is required, at least two years.
Suitable for a new or recent graduate; no prior professional experience required. Internships, academic projects, or other hands-on data work are a plus, as is a demonstrated ability to learn quickly.
Working proficiency in SQL and Python for data manipulation and analysis.
Some past experience working with and cleaning raw datasets.
Strong customer-service orientation and the ability to communicate clearly with non-technical clients.
Attention to detail, particularly around data quality, validation, and documentation.
Consistently takes initiative to learn and try new things, and keeps a mindset of constant improvement.
SQL
Python
Data cleaning & manipulation
Interpersonal skills

Nice To Haves

Exposure to Redshift, Snowflake, PostgreSQL, or similar databases.
Experience with Pandas DataFrames, Jupyter notebooks, and other quick data processing tools.
Familiarity with DBT, Airflow, or other data pipeline and orchestration tooling.
Familiarity with AWS S3 and Lambda functions, or other AWS services.
Experience working with Github, or another code version control platform.
Experience handling sensitive or confidential data and following security best practices.
Experience working in a startup, nonprofit, or mission-driven environment.

Responsibilities

Serve as the data engineering point of contact for new clients during onboarding, guiding them through what data to provide and in what format.
Receive incoming client data and perform exploratory data analysis to understand its structure, quality, and completeness.
Identify and flag missing, malformed, or inconsistent fields early, and coordinate with the client to resolve them.
Clean, transform, and validate client data so it conforms to CredLens' standards and is ready for downstream ingestion.
Build and run the models and transformations that move a client's data through the onboarding stages of the pipeline, using SQL, Python, DBT, and Airflow.
Document each client's onboarding: data sources, decisions made, and any exceptions, contributing to shared onboarding procedures and templates.
Track onboarding progress and time, and surface areas of friction to improve the process so that it becomes faster and more repeatable over time.
Work with the broader Data Engineering team to research, learn, and implement new improvements to the onboarding pipeline’s efficiency & scalability so it can grow with us.