Data Engineer
Procyon Corporation
Pittsburgh, PA, United States
3 months ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Compensation
$104,000.0 - $124,800.0
Working hours
Regular working hours
Job source
Tech stack
Airflow
Amazon Web Services
Microsoft Azure
Cloud Computing
Information Engineering
Data Governance
Extract Transform Load (ETL)
Data Mining
Data Security
Data Warehousing
Amazon DynamoDB
Python (Programming Language)
+18 more
PostgreSQL
Microsoft SQL Server
MongoDB
MySQL
NoSQL
Redis
SQL Databases
Scripting
Data Storage Technologies
Snowflake
Apache Spark
Cassandra
Apache Kafka
Non-relational Database
Data Management
Machine Learning Operations
Data Pipelines
Databricks
Job description
We are looking for a talented and experienced Data Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support business intelligence and analytics needs., * Design, develop, and maintain robust data pipelines and ETL/ELT processes
- Build and optimize data models for both relational and non-relational databases
- Write efficient Python scripts for data extraction, transformation, and loading
- Collaborate with data scientists and analysts to understand data requirements
- Ensure data quality, consistency, and integrity across all data platforms
- Monitor and troubleshoot data pipeline performance and failures
- Implement best practices for data security and governance
- Work with cloud platforms (AWS/Azure/GCP) for data storage and processing
Requirements
| Do you have experience in Schema design?, Skills: Python | SQL | NoSQL |
Experience: 3-7 Years Employment Type: Full Time / Contract Work Mode: Onsite, * Strong proficiency in Python for data engineering and automation
- Hands-on experience with SQL databases (PostgreSQL, MySQL, MS SQL Server)
- Experience with NoSQL databases (MongoDB, Cassandra, DynamoDB, or Redis)
- Knowledge of data warehousing concepts and data modeling
- Experience with big data tools (Spark, Kafka, Airflow) is a plus
- Familiarity with cloud platforms (AWS, Azure, or GCP)
- Strong problem-solving and analytical skills
Nice to Have
- Experience with dbt, Snowflake, or Databricks
- Knowledge of data governance and data quality frameworks
- Exposure to machine learning pipelines
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