Analyzing and interpreting data; Research operations, compliance, and oversight. [31]

Data Engineer

What this job does:

Design, build, and maintain reliable data infrastructure (pipelines, data models, storage, and governance) so analysts, scientists, and products can use trustworthy data at scale.

This job is similar to these lab/research tasks:

Pipelines: automate instrument exports and preprocessing; Data modeling: design tidy data tables, schema/metadata standards; Quality: QC checks, replicate tracking, audit trails; Stakeholders: build shared datasets for collaborators, train lab members on data tools; Other: maintain SOPs, debug failures, improve throughput.

Typical starting salary range

$103k to $170k

Salary estimate represented as 25th to 75th percentile. Gathered from recent job postings, Glassdoor.com, and Salary.com

Daily task breakdown

Pipeline engineering (ETL/ELT, integrations) 35%
Data modeling & platform architecture 20%
Data quality, monitoring & governance 20%
Stakeholder partnership (requirements, enablement) 15%
Other (documentation, incident response, mentoring) 10%.

Skills you'll use in this job:

SQL plus Python/Scala/Java; ETL/ELT and workflow orchestration (Airflow/dbt/Step Functions); cloud + data warehouses/lakes; data quality, monitoring, and governance; documentation and cross-functional collaboration.

Talk about these PhD skills in your resume and interview:

Translate your PhD to data reliability: careful recordkeeping, QC, automation, and reproducibility. Highlight building pipelines for instrument outputs, managing metadata, writing robust code with tests, and setting standards so others can trust and reuse data.