Data Engineer

Introduction

CyOpsPath is a high-priority & time sensitive job discovery and career intelligence platform designed to connect top-tier talent with immediate, mission-critical opportunities across the United States and India.

We function as a job directory listing urgent, time-sensitive openings from the top 10 job distributors and Clients.

Application Instructions - You will be redirected to the company page to complete the application in a new window.

Key Responsibilities

1. Pipeline Development: Design, construct, test, and maintain scalable data pipelines and ETL/ELT workflows using Python, SQL, and modern orchestration tools (e.g., Apache Airflow, Prefect).
2. Data Architecture: Manage and optimize data storage solutions, data warehouses (e.g., Snowflake, BigQuery, Redshift), and data lakes to ensure high availability and performance.
3. Data Governance & Quality: Implement data validation routines, monitor pipeline reliability, and enforce data security, privacy, and compliance standards.
4. Collaboration: Partner with cross-functional teams to understand business requirements, transform raw data into actionable datasets, and support analytics or machine learning initiatives.
5. Performance Optimization: Identify bottlenecks in database queries and data ingestion processes, refactoring code and schema designs for optimal throughput.

Required Qualifications

1. Technical Expertise: Advanced proficiency in Python (or Scala) and SQL.
2. Database & Warehouse Experience: Hands-on experience with modern cloud data warehouses (Snowflake, Google BigQuery, or AWS Redshift) and relational/non-relational databases (PostgreSQL, MongoDB, etc.).
3. Data Engineering Tools: Experience with data modeling, ETL/ELT pipeline tools, and workflow orchestrators (e.g., Airflow, dbt).
4. Cloud Infrastructure: Familiarity with AWS, GCP, or Azure services related to data management.
5. Best Practices: Strong understanding of version control (Git), CI/CD practices, and software development fundamentals.