Data Engineer

As a Data Engineer, you will design, develop, and optimize scalable data platforms and streaming solutions that support enterprise analytics, machine learning, and business intelligence initiatives. You will work within a highly collaborative engineering environment to build cloud-native data solutions, modernize legacy platforms, and improve data processing reliability, performance, and governance.

You will contribute to the design and implementation of high-volume data pipelines, real-time streaming frameworks, and lakehouse architectures using Google Cloud technologies and open-source data engineering tools. Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines using Spark, Kafka, and Flink.
• Build and support cloud-native data platforms on Google Cloud Platform (Google Cloud Platform), including BigQuery, Cloud Storage, Dataproc, and Cloud Composer.
• Develop and optimize data processing workflows using Python, PySpark, SQL, and related technologies.
• Implement modern data lakehouse architectures leveraging technologies such as Iceberg, Delta Lake, and Parquet.
• Collaborate with software engineers, architects, product teams, and business stakeholders to deliver reliable data solutions.
• Perform data migration and modernization initiatives from on-premises environments to cloud-native platforms.
• Improve data quality, governance, observability, and operational efficiency through automation and engineering best practices.
• Support CI/CD deployment processes and infrastructure automation for data platforms.
• Participate in code reviews, technical discussions, and continuous improvement initiatives.
• Research and evaluate new technologies and recommend solutions for complex data engineering challenges.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
• 5+ years of experience in Data Engineering or Software Engineering.
• 5+ years of hands-on experience with Hadoop ecosystems and Google Cloud data solutions.
• Experience building and supporting distributed data processing solutions using Apache Spark.
• 2+ years of experience developing streaming data solutions using Kafka, Flink, and Spark Streaming.
• 3+ years of experience designing and implementing data lakehouse architectures.
• Experience with:
• Python and PySpark
• Apache Kafka
• Apache Airflow
• SQL
• Google Cloud Storage
• BigQuery
• Dataproc
• Cloud Composer
• Experience working with NoSQL databases, including columnar, graph, document, and key-value databases.
• Strong understanding of scalable distributed computing and data engineering best practices., * Google Cloud Professional Data Engineer certification or equivalent cloud certification (AWS Specialty Data Analytics or Azure Data Engineer).
• Experience migrating large-scale data platforms from on-premises environments to Google Cloud.
• Strong knowledge of Hadoop ecosystem technologies, including:
• Hive
• HDFS
• Parquet
• Apache Iceberg
• Delta Lake
• Deep understanding of data warehouse architecture, cloud data platforms, data orchestration, and pipeline optimization.
• Experience designing highly scalable, modular, and governance-driven data frameworks.
• Knowledge of Generative AI frameworks such as LangChain and LangGraph for agent-based data applications.
• Experience with DevOps and CI/CD practices, including Git, Jenkins, Docker, and Kubernetes.
• Familiarity with React and Node.js development for data-focused web applications.

What You’ll Bring

• Strong problem-solving and analytical skills.
• Ability to work effectively in a fast-paced, highly collaborative environment.
• Excellent communication and stakeholder management capabilities.
• Passion for modern data architectures, cloud technologies, and continuous learning.