Data Engineer

Procyon Corporation
Pittsburgh, United States of America
17 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate
Compensation
$ 125K

Job location

Pittsburgh, United States of America

Tech stack

Airflow
Amazon Web Services (AWS)
Azure
Cloud Computing
Information Engineering
Data Governance
ETL
Data Mining
Data Security
Data Warehousing
Amazon DynamoDB
Python
PostgreSQL
Microsoft SQL Server
MongoDB
MySQL
NoSQL
Redis
SQL Databases
Scripting (Bash/Python/Go/Ruby)
Data Storage Technologies
Snowflake
Spark
Cassandra
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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