Role Overview:
As a Data Engineer IV, you will serve as a senior technical contributor responsible for designing, developing, and optimizing scalable cloud-based data solutions that enable enterprise analytics, reporting, and business intelligence. You will lead the delivery of complex data integration initiatives, establish engineering best practices, and provide technical guidance to other engineers. This role requires a strong understanding of modern data engineering practices and a conceptual understanding of data architecture principles to design scalable, maintainable, and governed data solutions while partnering with business and technology teams to solve complex business problems.
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
Skills Required
- Bachelor's degree in computer science, Information Technology, Engineering, or 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, and Step Functions.
- Advanced proficiency with SQL, Python, and PySpark for developing scalable ETL/ELT solutions.
- Practical experience applying AI/ML techniques to data engineering problems (automated data quality checks, anomaly detection, intelligent pipeline monitoring).
- Practical experience developing semantic and contextual layers within a data lakehouse.
- Strong understanding of dimensional modeling, data warehousing, data lakehouse concepts, Apache Iceberg, and Parquet.
- Experience integrating enterprise platforms such as Salesforce, NetSuite, and other SaaS applications.
- 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.
- Strong analytical, problem-solving, and communication skills with ability to collaborate across technical and business teams.
What We Do
PDI Technologies resides at the intersection of productivity and sales growth, delivering powerful solutions that serve as the backbone of the convenience retail and petroleum wholesale ecosystem. By “Connecting Convenience” across the globe, we empower businesses to increase productivity, make more informed decisions, and engage faster with their customers. www.pditechnologies.com
