Data Engineer IV

Key Responsibilities

Design, develop, and optimize scalable cloud-based data pipelines that support enterprise analytics, reporting, and business intelligence.

Lead the development of complex data integration solutions using AWS-native technologies and modern data engineering practices.

Design scalable data models, ingestion frameworks, and transformation processes that support reliable, high-quality analytics.

Apply data architecture principles when designing scalable, secure, and maintainable data pipelines, data models, and integration solutions.

Establish and promote engineering standards, reusable frameworks, and best practices that improve platform reliability, scalability, and maintainability.

Partner with business stakeholders to translate reporting and analytics requirements into technical solutions.

Drive continuous improvement through automation, infrastructure as code, CI/CD, and DevOps best practices.

Lead troubleshooting and resolution of complex data platform, integration, and performance issues.

Mentor and provide technical guidance to other data engineers through code reviews, knowledge sharing, and best practice adoption.

Evaluate emerging technologies and recommend improvements that enhance platform performance, scalability, security, and operational efficiency.

Support data governance initiatives by promoting data quality, metadata management, and engineering standards across the data platform.

Qualifications

Bachelor's degree in computer science, Information Technology, Engineering, or a related field, or equivalent practical experience.

8+ years of experience in data engineering, cloud data platforms, or related software engineering roles.

Expert experience developing cloud-based data solutions using AWS technologies such as S3, Glue, Redshift, AppFlow, Lake Formation, Step Functions, and related services.

Advanced proficiency with SQL, Python, and PySpark for developing scalable ETL/ELT solutions.

Practical experience with applying AI/ML techniques to data engineering problems, such as automated data quality checks, anomaly detection, or intelligent pipeline monitoring.

Practical experience developing semantic and contextual layers within a data lake house.

Strong understanding of dimensional modeling, data warehousing, data lakehouse concepts, Apache Iceberg, Parquet, and modern data engineering practices.

Experience integrating enterprise platforms such as Salesforce, NetSuite, and other SaaS applications.

Strong understanding of modern data engineering practices with conceptual knowledge of data architecture principles, including data modeling, governance, metadata management, and scalable cloud data platforms.

Experience with Infrastructure as Code (Terraform), Git, Azure DevOps, and CI/CD pipelines.

Demonstrated ability to mentor engineers, influence technical decisions, and drive engineering best practices without formal people leadership.

Strong analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.

Behavioral Competencies

Ensures Accountability

Manages Complexity

Communicates Effectively

Balances Stakeholders

Collaborates Effectively