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# Data Analytics Engineer

- **Company:** Veneer One Inc.
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, Cloud Computing, Databases, Customer Data Management, Data Validation, Data Cleansing, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Retrieval, Python (Programming Language), PostgreSQL, NumPy, Operational Data Store, Software Engineering, SQL Databases, Generative AI, Pandas, Pytest, Data Analytics, Data Management, Virtual Agents, Data Pipelines, Programming Languages
- **Published:** September 5, 2026
- **Apply:** https://www.dice.com/job-detail/1525f333-46fe-4345-b851-bccc2be581e5

## About the Role

* 5+ years of experience in a data-driven role or equivalent, including experience owning data initiatives and projects end to end
 * 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 at least three of the following technologies:
 + Database technologies (e.g., SQL, PostgreSQL)
 + Data science libraries (e.g., NumPy, pandas)
 + Data pipelining workflows and tools (e.g., Dagster, Airflow, dbt)
 + Cloud providers (e.g. AWS, Azure), including software development kits used to access data and services on these platforms
 + Ability to translate complex data findings into clear, compelling narratives
 * Strong communication capability to decompose complex operational workflows into clear, repeatable steps that both teammates and AI tools can act on
 * Passionate about data integrity, with a proven track record of transforming raw inputs into high quality, trusted datasets
 * A self-starter attitude and demonstrated ability to learn new technologies quickly
 * Experience in the following is a plus:
 + Generative AI tools (e.g. AWS Bedrock, LangChain)
 + Testing frameworks (e.g. pytest), * Self-motivated and passionate about leaving everything they touch better than how they found it
 * Firm believers that people should love what they do and are eager to build a culture that enables them to do their best work
 * Creative problem solvers who respectfully challenge the status quo in the pursuit of excellence
 * People who lead discussions with curiosity and value diverse perspectives
 * Eager to explore new ideas, understand the power of feedback, and constantly seek opportunities to grow and develop their skills
 * Strong team players who thrive in collaborative environments and celebrate the success of others

## Description

* Partner with VIA's client delivery team and customers to translate domain knowledge into data infrastructure requirements, validate assumptions, and resolve data-related issues
 * Explore customer data to build a clear picture of contents and characteristics (e.g. averages, expected ranges, trends, standard deviations) and make suggestions for data cleaning and analysis

Build AI-powered data products

 * Deliver data-based products to external customers, including interactive data analysis and investigation platforms, data quality reports, statistical analysis, and visualizations that turn complex findings into clear stories
 * Build AI into VIA's data products, for example through automated insights, anomaly detection, AI-assisted data quality checks, and natural-language interfaces over operational data
 * Evaluate the quality and reliability of AI/ML outputs against domain expectations, and design the human-in-the-loop checks that keep our data products trustworthy

Oversee data pipelines

 * Own the design, quality, and reliability of ETL/ELT pipelines, including work built with AI assistance
 * Coordinate with internal stakeholders and customers when information is missing or discrepancies are found
 * Run quality control on data and data products through both automated tests and targeted manual review, and document the assumptions and decisions made along the way so the work stays traceable

Improve the platform

 * Contribute to the continual improvement of internal tools for data cleaning, modeling and analytics, and data quality assessment by identifying key data-related challenges that are ideal candidates for automation and AI enhancement


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