Title: Senior Data Engineer - Hybrid
Location: Atlanta, GA, USA
Length: Long term
Restriction: W2 or C2C

Description:

Webcam interview; *** Long term project *** Hybrid - Need Locals

Job Responsibilities:

The Senior Data Engineer will play a key role in the Enterprise Data Services team, responsible for transforming data from various systems to provide insights and analytics for business stakeholders.
Leveraging cloud-based infrastructure, this role involves implementing scalable, resilient, and efficient technology solutions.
Collaboration with Data Engineers, Data Analysts, DBAs, cross-functional teams, and business leaders is essential.
You will architect, design, implement, and operate data engineering solutions using Agile methodology, empowering users to make informed business decisions.
You understand the full data lifecycle and recognize the importance of high-quality data across applications, machine learning, business analytics, and reporting. Ideal candidates possess strong critical thinking skills, the ability to solve complex problems, and the talent to transform data into valuable business solutions.
You demonstrate leadership and take ownership of technical projects in a fast-paced environment.

Candidates need to have:

At least 7 years of post-degree professional experience.
At least 5+ years of experience in the Airline industry.
4+ years of development experience building and maintaining ETL pipelines.
3+ years of experience with database technologies and data development, including Python, PLSQL, etc.
Proficiency in mentoring junior team members through code reviews and best practice recommendations.
Deep understanding of writing test cases to ensure data quality, reliability, and confidence.
Proven track record of advancing new technologies to improve data quality and reliability.
Continuous improvement of the quality, efficiency, and scalability of data pipelines.
Expertise in working with queries/applications, including performance tuning, using indexes, and materialized views to enhance query performance.
Ability to identify necessary business rules for extracting data and recognize related functional or technical risks (e.g., data latency, frequency).
Develop initial queries for profiling data, validating analysis, testing assumptions, driving data quality assessment specifications, and defining a path to deployment.
Familiarity with best practices for data ingestion and data design.