Data Engineer

Thermo Fisher Scientific•Pittsburgh, PA

3d•Remote

About The Position

The Data Engineer is an entry-level role supporting the development, maintenance, and optimization of data solutions using the Databricks platform. The engineer works within an Agile development team, collaborating with experienced data engineers, analysts, architects, and business partners to build reliable data pipelines, data models, and analytics-ready datasets. The role supports solutions across data ingestion, transformation, modeling, analytics, and reporting, including work for Finance, Supply Chain, and Operations. It also includes contributing to data quality, governance, security, documentation, production support, and troubleshooting.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, Information Systems, or a related field; equivalent education, technical training, certifications, internships, or relevant practical experience may also be considered.
0–2 years of experience in data engineering, software development, business intelligence, data analytics, data science, or a related technical field; relevant internships, academic projects, certifications, or hands-on project experience may count toward this requirement.
Foundational knowledge of Databricks or similar cloud-based data platforms.
Experience or coursework using SQL to query and transform 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.
Basic understanding of relational databases and data modeling.
Familiarity with cloud computing concepts and services, preferably AWS.
Familiarity with Git source control and software development practices.
Understanding of basic data quality concepts, including validation, completeness, consistency, and accuracy.
Strong analytical and problem-solving skills.
Ability to learn new technologies and work collaboratively within a technical team.

Nice To Haves

Experience with Databricks notebooks, workflows, jobs, or Databricks SQL.
Knowledge of Delta Lake and lakehouse architecture concepts.
Experience with PySpark DataFrames and Spark SQL.
Familiarity with Databricks Unity Catalog and data governance concepts.
Familiarity with AWS services such as S3, IAM, Glue, Athena, or related data services.
Experience with Apache Airflow or other workflow orchestration technologies.
Familiarity with CI/CD concepts and automated deployment practices.
Experience with data visualization tools such as Power BI.
Familiarity with streaming and event-driven technologies such as Apache Kafka.
Knowledge of data warehouse and data lake concepts.
Knowledge of data governance, metadata management, and access-control concepts.
Databricks or cloud-provider certifications.

Responsibilities

Develop and maintain data pipelines and transformations using Databricks, SQL, Python, and PySpark.
Assist with ingesting, transforming, and processing structured and semi-structured data from multiple source systems.
Develop and maintain Databricks notebooks, pipelines, workflows, and jobs for data engineering and analytics use cases.
Build and maintain data tables and datasets using Delta Lake and lakehouse architecture principles.
Write, test, troubleshoot, and optimize SQL queries for data processing, analysis, and reporting.
Support ETL/ELT pipelines that move and transform data across source systems, cloud storage, Databricks, and downstream applications.
Work within an Agile Scrum team to design, develop, unit test, debug, document, and maintain data solutions based on functional and technical requirements.
Collaborate with senior engineers and architects to follow established architecture, development standards, security requirements, and engineering best practices.
Assist with defining data models, data structures, transformation logic, and information flows.
Validate and troubleshoot data to help ensure data products are high quality, consistent, complete, and accurate.
Monitor data pipelines and Databricks workloads and assist with troubleshooting pipeline failures and data-related issues.
Participate in root-cause analysis and resolve data and application issues with cross-functional teams.
Use Git and established CI/CD practices to manage and deploy data engineering code and configurations.
Create and maintain technical documentation for data pipelines, transformations, data models, and other data products.
Collaborate with business and technical teams in areas such as Finance, Supply Chain, and Operations to understand requirements and deliver data solutions.
Develop knowledge of data governance, data security, access controls, and data quality management practices.
Participate in rotating production support activities as needed.