LOCATION: FORT MILL OR AUSTIN (HYBRID)

We are looking for a skilled Data Engineer to join our team and help build robust, scalable, and efficient data pipelines. The ideal candidate will have strong expertise in AWS, Python, Spark, ETL Pipelines, SQL, and Pytest. This role involves designing, implementing, and optimizing data pipelines to support analytics, business intelligence, and machine learning initiatives.

REQUIRED SKILLS & QUALIFICATIONS:

• 7-11+ years of experience in Data Engineering or a related field.
• Strong proficiency in AWS (S3, Glue, Lambda, EMR, Redshift, etc.) for cloud-based data processing.
• Hands-on experience with Python for data processing and automation.
• Expertise in Apache Spark for distributed data processing.
• Solid understanding of ETL pipeline design and data warehousing concepts.
• Proficiency in SQL for querying and managing relational databases.
• Experience writing unit and integration tests using Pytest.
• Familiarity with CI/CD pipelines and version control systems (e.g., Git).
• Strong problem-solving skills and ability to work in a fast-paced environment.

PREFERRED QUALIFICATIONS:

• Experience with Terraform, Docker, or Kubernetes.
• Knowledge of big data tools such as Apache Kafka or Airflow.
• Exposure to data governance and security best practices.
  Job Type: Full-time
  Pay: $110,000.00 - $140,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Paid time off
• Vision insurance

Application Question(s):

• This is a Full-Time position, are you still interested in applying for this role?
• Do you require any type of sponsorship to work in the USA?
• Please share your LinkedIn profile. (Mandatory requirement)

Experience:

• Data Engineer: 7 years (Required)

Work Location: Hybrid remote in Austin, TX 7870

• Design, develop, and maintain ETL pipelines using AWS services, Python, and Spark.
• Optimize data ingestion, transformation, and storage processes for high-performance data processing.
• Work with structured and unstructured data, ensuring data integrity, quality, and governance.
• Develop SQL queries to extract and manipulate data efficiently from relational databases.
• Implement data validation and testing frameworks using Pytest to ensure data accuracy and reliability.
• Collaborate with data scientists, analysts, and software engineers to build scalable data solutions.
• Monitor and troubleshoot data pipelines to ensure smooth operation and minimal downtime.
• Stay up-to-date with industry trends, tools, and best practices for data engineering and cloud technologies.