Senior Data Analytics Engineer — AI Data Platforms (Remote)

VIA; Washington; Hybrid; USD 140,000 - 160,000; Full time

Benefits offered by this job

Salary range $140,000-$160,000

Health benefits

Parental leave

Flexible vacation

Hybrid/remote work and multiple office

Mentorship programs

Job summary

VIA is seeking a Senior Data Analytics Engineer to translate domain knowledge into scalable data infrastructure and analytics platforms. You will partner with client delivery teams to define data requirements, validate assumptions, and build AI-powered data products that reveal actionable insights.

You will own ETL/ELT pipelines, ensure data quality, and collaborate with stakeholders to drive reliable data stories and visualizations across client environments.

Qualifications

• 5+ years of experience in a data-driven role or equivalent.
• Bachelor’s or Master’s degree in science, mathematics, engineering, or a data-driven field.
• Proficiency in Python, R, or equivalent programming language.
• Competence in three of: SQL/PostgreSQL, NumPy/pandas, Dagster/Airflow/dbt, AWS/Azure, translating data findings into narratives.

Responsibilities

• Translate domain knowledge into data infrastructure requirements with clients.
• Explore customer data to summarize contents, ranges, trends, and anomalies.
• Deliver AI-powered data products including interactive analysis and quality reports.
• Own ETL/ELT pipelines and coordinate with stakeholders on data quality and discrepancies.
• Improve internal tools for data cleaning, modeling, analytics, and data quality.

Skills

Python

R

Data storytelling

Data visualization

Communication

Education

Bachelor's or Master's degree in science/engineering/data-driven field

Tools

SQL

PostgreSQL

NumPy

pandas

Dagster

Airflow

dbt

AWS

Azure

LangChain

Job description

VIA is seeking a Senior Data Analytics Engineer to translate domain knowledge into scalable data infrastructure and analytics platforms. You will partner with client delivery teams to define data requirements, validate assumptions, and build AI-powered data products that reveal actionable insights.

You will own ETL/ELT pipelines, ensure data quality, and collaborate with stakeholders to drive reliable data stories and visualizations across client environments.