Associate Client Data Engineer

CredLens, LLC; Washington (District of Columbia); Hybrid; USD 74,850 - 91,700; Full time

Job summary

CredLens, LLC is hiring an Associate Client Data Engineer in Washington, DC. You will join a small Data Engineering team and focus on client onboarding, handling data from issuer partners with a strong emphasis on quality and reliability.

You will work with SQL, Python, DBT, and Airflow to move data through onboarding stages and document the process for future improvements. The role is early‑career, offering hands‑on experience, direct client interaction, and a clear path to broader data

Qualifications

• Bachelor's degree or equivalent practical experience in CS/IS/data science or related field.
• Experience or coursework showing data onboarding, cleaning, and validation is a plus.
• Strong SQL and Python skills for data manipulation and analysis.
• Excellent communication with non-technical clients and strong attention to data quality.

Responsibilities

• Serve as data engineering point of contact during client onboarding.
• Ingest, explore, and document incoming client data structure and quality.
• Clean, transform, and validate data to CredLens standards for downstream use.
• Build and run data transformations using SQL, Python, DBT, and Airflow.
• Document onboarding decisions, data sources, and exceptions for repeatable processes.
• Track onboarding progress and identify friction to improve speed and reliability.
• Collaborate with Data Engineering to improve onboarding pipeline efficiency and scalability.

Skills

SQL

Python

Data cleaning

Interpersonal skills

Education

Bachelor's degree or equivalent practical experience

Tools

Redshift

Snowflake

PostgreSQL

Pandas

Jupyter

DBT

Airflow

AWS S3

AWS Lambda

GitHub

Job description

About the Role

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's 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.

Context

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.

Key Responsibilities

Core responsibility areas, listed below with the approximate time required for execution in the first 12 – 18 months of work if the incumbent is new to the role:

• 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.

Qualifications and Experience

Education

• 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.

Experience Required

• 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.

Experience Preferred

• 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.

Skills Required

• SQL
• Python
• Data cleaning & manipulation
• Interpersonal skills

About CredLens

Non‑degree credentials are reshaping how people move from learning to earning. Millions of learners are pursuing certificates, bootcamps, and workforce programs as pathways to better jobs and higher wages. But growth has outpaced clarity.

Thousands of offerings, uneven definitions, and fragmented outcomes data make it nearly impossible to distinguish impact from activity. When results are unclear, learners carry the risk, strong programs can't stand out, and investment can't reliably flow to what works.

CredLens exists to fix that.

We are a neutral, trusted source of high‑quality outcomes data for non‑degree credentials — connecting issuer‑verified credential records to real‑world employment, earnings, and education outcomes. Our partners use that data to improve programs, guide investment, and strengthen career pathways for learners.

Two years in, we have a firmly established platform, a growing client base, and the backing of Strada Education Foundation. We are not a startup finding its footing. We are a company beginning to scale — and we are building the team that will take us there.

Mission and Value Alignment

Committed to providing equitable pathways to opportunity through postsecondary education and training (PSET), particularly for individuals who have faced significant barriers. Demonstrated alignment with CredLens' guiding values, commitment to building a strong and healthy workplace culture, and working in a collaborative environment.

Diversity, equity, and inclusion are central to CredLens' organizational vibrancy, employee experience, and mission. We strongly encourage applicants from people of color/the global majority, immigrant, bilingual, and bicultural individuals; people with disabilities, members of the LGBTQIA2S+ and gender nonconforming communities; and people with other diverse backgrounds and lived experiences. We believe every member on the team enriches our workplace by exposing us to a broad range of ways to understand and engage with the world, identify challenges, and discover, design, and deliver critical insights and solutions.

Travel Requirements

Approximately 5% domestic travel anticipated.

Compensation

$74,850 - $91,700 a year plus annual bonus.

The pay range listed is based on national compensation benchmark data and may vary depending on skills, experience, job‑related knowledge, variations in cost of labor, and in some cases, geographic location. The exact job offer will be determined based on several factors such as the candidate's individual skills, qualifications and experience relative to the requirements of the role. The range displayed with the job posting represents the minimum and maximum target for new hire salaries for the position across the U.S.

The company also reviews and considers internal equity (current employee salary) when hiring new employees to the organization. The range is the expected starting base salary for someone hired into this position with room to grow professionally, including increased earning potential beyond the starting pay range. Beyond a new hire's base salary, CredLens also offers all full‑time employees a comprehensive employee benefit package.