Data Engineer

Thermo Fisher

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Hybrid

Quick summary

Work type: Hybrid
Location: Pittsburgh, PA
Employment: Full-time
Posted: 4 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $168k

$116k $218k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$208,293.

Based on 240 similar postings.

Employer

About Thermo Fisher

Thermo Fisher Scientific is the world leader in serving science, providing analytical instruments, equipment, reagents, consumables, software, and services for life sciences research, pharmaceutical manufacturing, and diagnostics. Industry: Life Sciences & Laboratory Equipment

Thermo Fisher currently has 14 open roles on FindRole.

Listed pay typically runs $107,500–$165,000 across 6 roles with salary data.

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At a glance

TL;DR · Data Engineer

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JOB TITLE: Data Engineer The Data Engineer joins the Thermo Fisher Scientific team in an entry-level role to support the development, maintenance, and optimization of data solutions using the Databricks platform. Working within an Agile Scrum team, the engineer builds reliable data pipelines, data models, and analytics-ready datasets. Daily responsibilities include developing data transformations, managing ingestion from multiple source systems, and maintaining Databricks notebooks, workflows, and jobs. The role requires technical proficiency in SQL, Python, PySpark, and Delta Lake within a lakehouse architecture. Candidates must utilize Git and CI/CD practices to manage code while ensuring data quality, consistency, and accuracy. The position involves solving technical problems for Finance, Supply Chain, and Operations by designing information flows and supporting ETL/ELT processes across cloud storage and downstream applications.

What does a Data Engineer earn?

Median $159000 from 203 postings across 58 companies.

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Skills

Databricks SQL Python PySpark Delta Lake AWS ETL ELT Git CI/CD Apache Spark S3 Glue Athena Power BI Apache Kafka Data Modeling Agile Scrum

What you'll do

Develop and maintain data pipelines and transformations using Databricks, SQL, Python, and PySpark.
Ingest and process structured and semi-structured data from multiple source systems.
Build and maintain data tables and datasets using Delta Lake and lakehouse architecture.
Write, test, troubleshoot, and optimize SQL queries for data processing and reporting.
Develop ETL/ELT pipelines to move data across cloud storage, Databricks, and downstream applications.
Perform data validation and troubleshooting to ensure the quality and accuracy of data products.
Manage and deploy data engineering code and configurations using Git and CI/CD practices.
Create and maintain technical documentation for data pipelines, transformations, and data models.

What we're looking for

Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, Information Systems, or a related field.
0–2 years of experience in data engineering, software development, business intelligence, data analytics, data science, or a related technical field.
Foundational knowledge of Databricks or similar cloud-based data platforms.
Experience or coursework using SQL for querying and transforming data.
Experience or coursework using Python for data processing or software development.
Familiarity with Apache Spark or PySpark concepts.
Understanding of ETL/ELT and data pipeline concepts.
Familiarity with cloud computing concepts and services, preferably AWS (preferred).

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