Data Engineer, Forward Deployed Applications

About the Company

At Multiscale Technologies, we believe the future is driven by innovative materials — but traditional approaches to discovering and manufacturing them are outdated and unsustainable. That’s why we’ve developed cutting-edge technologies to revolutionize this process.

We bring together world-class materials scientists, software engineers, and AI experts to build intelligent systems that drive industrial transformation. As a Series-A funded startup, Multiscale offers you the opportunity to make a direct impact at a fast-growing company solving real-world engineering challenges.

About the Role

We are looking for a senior data engineer with 5–7 years of experience to join our Forward Deployed Applications team. This is a customer-facing, project-owning role, you will work directly with engineers and domain experts at client sites to explore raw datasets, design transformation workflows, and deliver production-ready pipelines.

While your primary focus will be data architecture, exploration, and transformation, you will also build Streamlit applications to expose processed data in intuitive, usable ways. You will deliver your own projects end-to-end from discovery to deployment and work independently alongside backend engineers who focus more on application-heavy builds.

Why you will want this job

You will have a direct hand in turning messy, unstructured industrial data into actionable insights that power AI models and decision-making tools. You will be embedded with customer teams, influencing both the data foundation and the applications that depend on it. And you will see your work in production quickly, often within days or weeks.

Responsibilities

Partner with customer engineers and domain experts to understand data sources, quality challenges, and integration requirements.

Design and build ETL / ELT workflows for structured and unstructured datasets, aligning them to analytics, ML, and simulation needs.

Develop and maintain scalable data pipelines with orchestration tools like Airflow or Prefect.

Create and maintain data validation, profiling, and observability systems.

Build and maintain data cataloging, lineage, and metadata tracking to improve discoverability and governance.

Design schemas and data models that support downstream AI and application workflows.

Build Streamlit applications for data exploration, visualization, and decision support, with an emphasis on showcasing processed, validated datasets.

Contribute data-related features and integrations back to AppHub when needed.

Operate independently on projects while collaborating closely with backend engineers, platform teams, and AI teams.

What we are looking for

5–7 years of professional data engineering experience.

Proven track record of delivering production-grade data workflows and architectures.

Strong skills in Python, Pandas, SQL, and at least one orchestration framework (Airflow, Prefect, Dagster, etc.).

Experience with database systems (SQL / NoSQL) and cloud storage solutions.

Comfortable engaging directly with customers in technical discussions and solution design.

Clear, concise technical communication skills.

Nice to Have

Experience in microelectronics or advanced manufacturing domains.

Exposure to BI tools and advanced data visualization techniques.

Experience with ML feature stores or analytics-focused data modeling.

Skills Must Have :

Python, SQL, ETL pipeline development, Data modeling, Cloud data platforms (AWS / GCP / Azure), Version control (Git), Data warehousing (Snowflake, BigQuery, Redshift), Batch and streaming data processing.

Preferred Skills :

Apache Spark, Apache Kafka, Data lake architectures, Docker, Kubernetes, Workflow orchestration (Airflow, Luigi), API integration