Cloud engineer in data platforms

Interested in building the next generation cloud data ecosystem that processes petabytes of data and powers AI-driven insights for some of the world's largest brands? The Marketing Services Technology team is transforming its analytics and data platform from traditional on-premises Hadoop environments to a modern cloud-native architecture powered by AWS, Databricks, Apache Spark, Iceberg, and AI technologies. We are looking for a Lead Cloud Data Engineer who is passionate about cloud engineering, Big Data, distributed systems, and modern data platforms.

• Design, develop, and support large-scale cloud-native data solutions on AWS and Databricks.
• Build and maintain batch, streaming, and real-time data pipelines supporting analytics, reporting, machine learning, and AI workloads.
• Engineer scalable distributed data processing systems using Apache Spark and related Big Data technologies.
• Design and implement modern Lakehouse architectures leveraging Databricks, Delta Lake, and Apache Iceberg.
• Lead modernization initiatives migrating data workloads from Hadoop-based platforms to cloud-native architectures.
• Develop reusable frameworks and engineering patterns that improve scalability, reliability, and operational efficiency.
• Partner with Architects, Product Managers, Data Scientists, and Software Engineers to define and implement cloud data solutions.
• Evaluate technologies and approaches for data ingestion, transformation, storage, governance, and consumption.
• Implement best practices around security, governance, performance optimization, observability, and cost management.
• Drive automation using Infrastructure-as-Code, CI/CD pipelines, and modern software engineering practices.
• Contribute to the evolution of Marketing Services' cloud and data strategy while enabling future AI and machine learning capabilities.

• Strong hands-on experience with Big Data technologies including Hadoop, HDFS, Hive, Spark, and related distributed computing frameworks.
• Deep expertise in cloud data engineering on AWS, including storage, compute, networking, security, and scalable data architectures.
• Experience designing and developing modern data solutions using Databricks, Spark, Delta Lake, and cloud-native technologies.
• Proficiency in Python, Java, or Scala, building large-scale distributed data processing applications.
• Experience building robust batch and streaming pipelines supporting high-volume and high-velocity workloads.
• Understanding of modern Lakehouse architecture principles and open-table formats such as Apache Iceberg and Delta Lake.
• Skilled in data modelling, schema design, partitioning strategies, query optimization, and performance tuning.
• Familiarity with enterprise-scale data governance, security, privacy, and compliance, working with structured and unstructured datasets at scale.
• Experience with Infrastructure-as-Code (e.g., Terraform), modern CI/CD, and cloud-native monitoring and observability practices.
• Strong analytical and problem-solving skills, a passion for automation, and the ability to assess emerging technologies and recommend scalable, cost-effective solutions.
• Excellent communication and collaboration across technical and business teams; comfortable in Agile/Scrum environments managing multiple priorities.
• BS/MS degree in Computer Science, Software Engineering, Information Systems, or a related field.
• Будет плюсом: Experience with Databricks on AWS in enterprise production environments; Experience migrating workloads from Hadoop or Cloudera ecosystems to modern cloud architectures; Hands-on experience with Apache Kafka, Flink, or other event-streaming technologies; Experience supporting Machine Learning, MLOps, or AI-driven data platforms; AWS and/or Databricks certifications; Familiarity with FinOps and cloud cost optimization practices; Experience working with petabyte-scale data environments

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