Data Engineer

CyOpsPath 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.

At CyOpsPath, we actively track high-priority roles to drastically improve response rates for candidates. When you consistently apply to 7 tailored job roles daily for 30 days through our platform, our track record shows you will receive direct candidate updates and hear back from client hiring teams within 45 days.

Data Engineer

About the Role We are seeking a detail-oriented Data Engineer to design, build, and maintain our data pipelines and infrastructure. In this role, you will work closely with cross-functional teams to transform raw data into scalable, reliable, and accessible datasets that drive business decision-making and analytics.

Responsibilities

1. Build, optimize, and maintain scalable ETL/ELT pipelines to ingest data from diverse sources.
2. Design, construct, and manage reliable data architectures, data warehouses, and data lakes.
3. Collaborate with data scientists, analysts, and software engineers to deliver clean, structured data for reporting and modeling.
4. Implement data quality checks, monitoring systems, and data governance best practices to ensure high data integrity.
5. Optimize database performance, query execution, and storage costs across data platforms.
6. Troubleshoot data pipeline failures, identify bottlenecks, and drive continuous performance improvements.

Requirements

1. Proven experience as a Data Engineer or in a similar data engineering role.
2. Strong proficiency in SQL and programming languages such as Python, Scala, or Java.
3. Hands-on experience with relational databases (e.g., PostgreSQL, MySQL) and cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
4. Familiarity with workflow orchestration tools (e.g., Apache Airflow, Prefect, Dagster) and data transformation frameworks (e.g., dbt).
5. Experience working with cloud platforms (AWS, GCP, or Azure).
6. Knowledge of data modeling techniques (Star Schema, Data Vault, or relational modeling).
7. Solid understanding of Version Control (Git) and Agile development practices.

Preferred Qualifications

1. Experience with distributed data processing engines like Apache Spark or PySpark.
2. Familiarity with real-time streaming technologies such as Apache Kafka or AWS Kinesis.
3. Understanding of Infrastructure as Code (e.g., Terraform) and CI/CD pipelines for data solutions.