Data Engineer

Job description

Summary

Build and operate the R&D data foundation for battery research. You will own database systems (MySQL/Postgres/MongoDB), build reliable data pipelines that clean/split/load multi-source data into curated datasets, and deliver code-based internal dashboards. You will also apply advanced automation (agent-based workflows; OpenClaw or similar as a plus) to reduce manual effort in data cleaning, validation, and reporting, integrated into day-to-day ETL/DQ operations.

Responsibilities

Database operations and optimization: schema design, indexing/partitioning, query tuning, backup/restore, monitoring/alerting, and routine maintenance for databases.

Data pipelines

build batch/incremental jobs to ingest R&D data from multiple sources, perform cleaning/standardization, split/route data by rules, and load into curated tables/collections and views.

Data modeling and dataset delivery

design subject-area datasets and a lightweight metrics/KPI layer in the database to support analytics and modeling.

Data quality and regression

implement validation rules (units/definitions, completeness, consistency, ranges), generate quality reports, and run regression checks for changes in schema/logic.

Code-based visualization

build and maintain a small set of high-impact internal dashboards/apps for key R&D metrics and data health.

Advanced efficiency

use agent-based automation to accelerate repetitive tasks such as schema mapping, cleaning-rule generation, anomaly triage, documentation/runbook drafts, and QA report generation.

Collaboration

work with scientists/engineers to clarify definitions, iterate on data models, and improve data usability.

Requirements

Strong Python and SQL; able to independently implement reliable data processing/loading jobs and maintain production code. Hands-on experience with at least two of the following: MySQL, PostgreSQL, MongoDB; comfortable with database troubleshooting and performance tuning. Practical experience with ETL/ELT, data modeling, and working with messy real-world R&D/industrial datasets.

Engineering fundamentals

job scheduling, logging, monitoring, error handling, version control, and documentation.

Preferred

Stream processing or modern data stack exposure (e.g., Kafka, Spark/Flink, Trino/Presto). Streamlit (or similar) app development and basic frontend sense for internal tools. Agent/automation frameworks (e.g., OpenClaw, LangChain, LlamaIndex) used for developer productivity or data workflow automation. Experience with time-series/curve data, lab/test data, or scientific data formats.