Cloud Data Engineer

RESUMES IN ENGLISH PLEASE JOB SUMMARY The Senior Cloud Data Engineer leads the design, architecture, and implementation of secure, scalable data solutions on AWS, utilizing Snowflake, dbt, and modern automation tools. This role drives best practices for data quality, validation, and governance, while optimizing performance and reliability across both cloud and legacy platforms. The Senior Cloud Data Engineer collaborates closely with cross-functional teams to translate business requirements into robust data pipelines and products, mentor junior engineers, and champions continuous improvement in engineering standards and operational excellence. DUTIES & RESPONSIBILITIES Leads the design, development, and maintenance of highly scalable, secure, and resilient data pipelines and systems on cloud platforms. Architects and implements advanced data integration solutions to support complex analytics, business intelligence, and enterprise reporting needs. Collaborates with cross-functional teams, including data scientists, business analysts, product owners, and application engineers—to understand and deliver on evolving data requirements. Ensures the highest standards of data quality, integrity, and security through comprehensive testing, validation, and robust governance practices. Monitors, optimizes, and scales data workflows to achieve performance, reliability, and cost-efficiency targets across both cloud and legacy environments. Proactively identifies, investigates, and resolves data-related issues, providing technical leadership, mentorship, and guidance to junior team members. Champions continuous improvement by staying current with industry trends, emerging technologies, best practices, and by driving innovation in data engineering processes and solutions. Partners with enterprise architecture and data governance teams to define and enforce data standards and compliance requirements. Leads design reviews and technical workshops to uplevel engineering practices across the organization. Contributes to strategic planning for data platform evolution and modernization initiatives. QUALIFICATIONS DEGREE TYPE: Bachelor's Degree FIELD(S) OF STUDY: Bachelor’s degree in computer science, engineering, or other related field of study; or any combination of relevant work experience and education EXPERIENCE Minimum of 7 years of progressive experience in data engineering, with a strong emphasis on designing and implementing cloud-based solutions. Advanced proficiency in SQL, with hands-on experience in both relational (e.G., Oracle, SQL Server, MySQL, Snowflake) and NoSQL (e.G., MongoDB, DynamoDB) databases. Extensive experience architecting and operating data platforms on AWS and Azure; relevant cloud certifications (e.G., AWS Certified Data Analytics, Azure Data Engineer Associate) strongly preferred. Deep knowledge of data warehousing concepts, ETL/ELT processes, data modeling, and enterprise data governance frameworks. Demonstrated success in leading and mentoring data engineering teams, driving technical excellence and fostering collaborative environments. Exceptional problem-solving skills, attention to detail, and the ability to work independently and proactively in fast-paced settings. Outstanding communication, leadership, and cross-functional collaboration abilities, with a proven track record of partnering with business and technical stakeholders. COMPETENCIES/SKILLS Cloud Platform (AWS): Extensive hands-on experience architecting and operating data solutions on AWS; relevant AWS certifications (e.G., AWS Certified Data Analytics, AWS Solutions Architect) strongly preferred. Data Warehousing: Deep expertise in data warehousing concepts, dimensional modeling, ETL/ELT processes, and enterprise data governance, with a focus on AWS-native and integrated solutions. Programming Languages: Advanced proficiency in Python and Bash for automation, orchestration, and data processing tasks within AWS environments. Data Integration: Proven ability to design and implement advanced data integration solutions supporting complex analytics and business intelligence requirements on AWS. Database Management: Strong experience with both relational (e.G., Oracle, SQL Server, MySQL, Snowflake) and NoSQL (e.G., DynamoDB) databases, including AWS-native database services. Data Quality & Security: Demonstrated commitment to maintaining high data quality, integrity, and security in AWS cloud and hybrid environments. Tools & Technologies: Expertise with Snowflake, dbt, GitHub, Control-M, and familiarity with modern data engineering frameworks (e.G., Airflow, Fivetran, Matillion) as deployed on AWS. Infrastructure: Solid understanding of both traditional on-premises (virtual/physical) and AWS cloud-based infrastructure, including Windows and Linux environments. Open Source & AWS Services: Working knowledge of a variety of open-source tools and AWS services for data engineering, automation, and orchestration. Leadership & Mentorship: Proven track record of leading design efforts, mentoring engineers, and fostering technical excellence within teams. Problem-Solving: Exceptional analytical and problem-solving skills, with meticulous attention to detail. Collaboration: Outstanding communication, leadership, and cross-functional collaboration abilities, partnering effectively with business and technical stakeholders. Continuous Improvement: Commitment to staying current with AWS advancements, industry trends, and driving innovation in data engineering practices.