DE&A - Core - Cloud Data Engineer Professional

We are seeking a highly motivated Data Engineer to design, build, and maintain scalable data platforms and pipelines that enable analytics, reporting, machine learning, and AI-driven business solutions. The ideal candidate will have expertise in data integration, cloud technologies, data warehousing, and modern data engineering practices, with a strong focus on data quality, performance, and reliability.

The Data Engineer will work closely with business stakeholders, data architects, analysts, data scientists, and application teams to deliver trusted and scalable data solutions.

Education

• Bachelors degree in Computer Science, Information Technology, Engineering, Data Science, or related field.
• Masters degree is a plus.

Experience

• 12+ years of experience in Data Engineering, Data Warehousing, or Big Data environments.
• Experience building and supporting large-scale enterprise data platforms.
• Experience working in Agile development environments.

Technical Skills Programming Languages

• Python
• SQL
• Java (Preferred)

Data Engineering Tools

• Apache Spark
• Databricks
• dbt

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)

Data Platforms

• Snowflake
• Azure Synapse Analytics
• Amazon Redshift
• BigQuery
• Teradata

Databases

• SQL Server
• Oracle
• PostgreSQL
• MySQL
• MongoDB

Business Intelligence Tools

• Tableau
• Power BI
• Looker
• Qlik Sense

DevOps & Version Control

• Git
• Azure DevOps
• Jenkins
• Terraform
• Docker
• Kubernetes

Preferred Skills

• Data Lakehouse Architecture
• Real-Time Streaming Architecture
• Data Mesh Concepts
• Master Data Management (MDM)
• Data Governance Frameworks
• Machine Learning Data Pipelines
• API Development & Integration
• Event-Driven Architecture

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to work independently and collaboratively within cross-functional teams.
• Strong documentation and presentation skills.
• Attention to detail and commitment to delivering high-quality solutions.

Preferred Certifications Cloud Certifications

• Microsoft Azure Data Engineer Associate (DP-203)
• AWS Certified Data Analytics Specialty
• Google Professional Data Engineer

Platform Certifications

• Snowflake SnowPro Certification
• Databricks Certified Data Engineer Associate
• Tableau Certified Professional (Preferred)

Data Pipeline Development

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Build and optimize batch and real-time data ingestion frameworks.
• Integrate data from multiple sources, including databases, APIs, SaaS applications, and third-party systems.
• Ensure data consistency, quality, and reliability across platforms.

Data Platform Engineering

• Develop and support enterprise Data Lake, Data Warehouse, and Lakehouse architectures.
• Implement scalable data solutions on cloud platforms.
• Design data models to support reporting, analytics, and AI/ML use cases.
• Optimize data storage, processing, and query performance.

Data Quality & Governance

• Establish data validation, cleansing, and monitoring processes.
• Implement data governance, lineage, cataloging, and metadata management practices.
• Ensure compliance with security, privacy, and regulatory requirements.
• Support audit and data quality initiatives.

Performance Optimization

• Monitor and tune data pipelines and databases for performance and scalability.
• Identify and resolve data bottlenecks and processing issues.
• Optimize SQL queries and large-scale data transformations.

Collaboration & Stakeholder Engagement

• Collaborate with Data Architects to implement enterprise data solutions.
• Work closely with BI developers, analysts, and data scientists to support analytical needs.
• Participate in requirements gathering, solution design, and technical reviews.
• Provide production support and troubleshooting for critical data platforms.

DevOps & Automation

• Implement CI/CD pipelines for data engineering solutions.
• Automate deployment, monitoring, and operational processes.
• Support infrastructure-as-code and cloud automation initiatives.