Big Data Architect

Position Purpose:
We are seeking a highly skilled and experienced Big Data Architect to join our international team. You will play a pivotal role in shaping our Big Data environments and projects, including the Global Data Lake, while enhancing our Sustainable Estimatics offerings. Sustainable Estimatics is a leading suite within the company, recognized for its substantial impact on the industry. With our innovative and certified algorithms, we provide our customers with significant cost savings by minimizing waste and optimizing resource usage. By embedding sustainability principles into our Estimatics practices, we actively contribute to the industry's collective effort to reduce environmental impact. Our commitment to sustainability goes beyond individual projects; we aim to drive industry-wide innovation through the continuous development of new technologies and practices that create a positive ripple effect for both the environment and society.

As a Big Data Architect, you will be responsible for designing the overall architecture of our data systems, ensuring they are robust, scalable, and efficient. You will develop architectural strategies and frameworks that guide our data processing initiatives, enabling the effective management of large volumes of data from diverse sources worldwide.

What You Will Be Doing:

• Design and implement scalable and efficient data architectures that support data processing pipelines using Cloudera, Spark, and other relevant technologies.
• Lead the development of scalable API solutions to facilitate Data as a Service (DaaS), providing seamless access to data for both external and internal customers.
• Establish best practices for data ingestion, transformation, and storage processes to ensure data quality, integrity, and availability across international locations.
• Collaborate with cross-functional teams to gather business requirements and translate them into comprehensive architectural specifications for data processing and analysis.
• Optimize data workflows and the performance of Spark jobs to ensure they meet stringent latency and throughput requirements while processing massive datasets.
• Conduct troubleshooting and performance tuning of Cloud or On-premises infrastructure to identify performance bottlenecks and enhance resource utilization.
• Leveraging tools like New Relic for performance monitoring and Graylog for log analysis.
• Work closely with data scientists and analysts to ensure timely and reliable data sets for advanced analytics and machine learning models.
• Implement data governance practices and ensure compliance with data privacy and security regulations across various regions.
• Stay abreast of emerging technologies and industry trends related to big data processing, Cloudera, and Spark, and propose innovative architectural solutions to enhance data processing capabilities.
• Provide technical leadership, mentorship, and guidance to engineering teams, fostering a collaborative and innovative culture within the international group.
• Participate in agile development practices, including sprint planning, architecture reviews, and continuous integration and deployment, to ensure high-quality software delivery.

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• A minimum of 7 years of working experience in big data architecture, preferably with Cloudera and Spark technologies.
• Strong understanding of API architectures and best practices, with experience in developing APIs for Data as a Service (DaaS) solutions.
• Strong proficiency in programming languages such as Scala, Python, or Java, with the ability to design and implement complex data solutions.
• In-depth knowledge of distributed computing principles and frameworks, including Hadoop and Spark.
• Extensive experience with Cloudera distribution and tools like HDFS, Hive, Impala, and HBase.
• Strong understanding of data modeling and database design principles, including schema design, partitioning, and indexing.
• Solid understanding of SQL and NoSQL databases, data warehousing concepts, and ETL processes.
• Proven expertise in designing, implementing, and optimizing data pipelines using Spark Streaming, Spark SQL, or other Spark modules.
• Familiarity with data ingestion techniques and tools, such as Kafka, Flume, Sqoop, or Nifi.
• Experience with cloud platforms like AWS or Azure, and knowledge of containerization technologies like Docker or Kubernetes is a plus.
• Understanding of data governance, data privacy, and security practices, particularly in an international context.
• Excellent problem-solving and analytical skills, with the ability to design solutions that optimize data processing workflows.
• English is required, with communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders.

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