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Data Engineer

Siri InfoSolutions Inc
West Chester, PA, United States
7 days ago
Apply on www.careerjet.com
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
www.careerjet.com

Tech stack

Application Programming Interfaces (APIs) Databases Data Validation Extract Transform Load (ETL) Relational Databases Distributed Computing Environment Python (Programming Language) Performance Tuning SQL Databases Teradata SQL Snowflake Apache Spark
+5 more
Git Pyspark Star Schema Software Version Control Data Pipelines

Job description

Pipeline Development: Design and implement robust ETL/ELT pipelines using PySpark and SQL to ingest data from diverse sources including APIs, flat files, and relational databases Data Modeling: Develop and manage complex data models (e.g., Star/Snowflake schemas) and maintain Fact and Dimension tables within Snowflake Performance Optimization: Monitor and tune Snowflake queries and Spark jobs to optimize performance, reduce latency, and manage computational costs Data Quality & Integrity: Implement automated data validation frameworks and testing procedures to ensure a “single source of truth” and high data reliability Operations: Troubleshoot production pipeline issues, manage version control via Git

Requirements

Must Have Technical/Functional Skills Snowflake: Deep expertise in Snowflake PySpark: Strong hands-on experience using Apache Spark with Python for distributed data processing and transformation. Database & SQL Knowledge: Advanced proficiency in Teradata

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Apply on www.careerjet.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

3:04 min

Database evolution and the funding behind vector databases

Erik Bamberg · LIVE

2:57 min

Core technical practices for robust data engineering

Sandhya Menon Sandhya Menon · World Congress 2026 Europe

56 sec

Favorite git commands and the importance of patch commits

Eileen Uchitelle Eileen Uchitelle +1 · Coffee With Developers

56 sec

Introduction to analytical data formats for software developers

Matthias Niehoff Matthias Niehoff · World Congress 2026 Europe

Videos

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PySpark - Combining Machine Learning & Big Data thumbnail
43:26
LIVE • almost 6 years ago

PySpark - Combining Machine Learning & Big Data

Ayon Roy

From Messy Queries to Scalable Systems - How Data Engineering actually works thumbnail
31:15
World Congress 2026 Europe • about 2 months ago

From Messy Queries to Scalable Systems - How Data Engineering actually works

Sandhya Menon

How a Small Team Shrank a Microsoft Monorepo by 94% thumbnail
33:23
Coffee With Developers • almost 2 years ago

How a Small Team Shrank a Microsoft Monorepo by 94%

Jonathan Creamer

Kubernetes and Microservices with Multi-Model Databases thumbnail
29:30
World Congress 2022 • about 4 years ago

Kubernetes and Microservices with Multi-Model Databases

Wei Hu

Data Science in Retail thumbnail
58:21
LIVE • over 3 years ago

Data Science in Retail

Julian Joseph

Branch your database like your code: How schema changes and pull requests go hand in hand thumbnail
48:05
LIVE • over 4 years ago

Branch your database like your code: How schema changes and pull requests go hand in hand

Johannes Nicolai, Lilli Seyther-Besecke

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