Senior Data Platform Engineer - job 1 of 2

As a Senior Data Platform Engineer at BigID, you will be responsible for designing, developing, maintaining, and troubleshooting our big data architecture. Main tasks include implementing data processing pipelines using Kafka for real-time streaming, optimizing search capabilities with Elastic technologies, and collaborating with cross-functional teams to translate requirements into technical specifications. Your role will also involve overseeing code reviews and ensuring the adherence to best practices.

To qualify for the Senior Data Platform Engineer role at BigID, candidates should have a Bachelor’s degree in Computer Science, Engineering, or a related field, with a Master's degree preferred. Additionally, you should possess at least 8 years of experience in software engineering, with a minimum of 5 years focusing on big data technologies like Elastic and Kafka. Proficiency in Java programming and a solid understanding of data modeling and ETL processes is also essential.

A Senior Data Platform Engineer at BigID should be well-versed in big data technologies such as Kafka and Elasticsearch, and have a strong proficiency in Java and its related frameworks. Experience with Docker, Kubernetes, cloud platforms like AWS or Azure, and familiarity with CI/CD principles are also advantageous to excel in this role.

Working at BigID as a Senior Data Platform Engineer means being part of a people-centric and inclusive culture that values innovation, collaboration, and diversity. We promote a flexible work environment that accommodates personal needs, along with office perks such as a dog-friendly atmosphere, wellness programs, and opportunities for career development. Our team thrives on making a positive impact and supporting each other’s growth.

At BigID, we believe that our team's happiness and health are paramount. As a Senior Data Platform Engineer, you'll benefit from equity participation, flexible work arrangements, and a range of perks including a fully stocked fridge, gym facilities, weekly happy hours, and evening meals. Additionally, we offer knowledge training and structured career development tracks to help you reach your professional goals.

When discussing your experience with Kafka, focus on specific projects where you implemented real-time data streaming solutions. Highlight your understanding of Kafka's role in data processing pipelines and how you have optimized its performance in a production environment. Be prepared to explain the architecture of your solutions and any challenges you faced.

To ensure code quality in big data projects, I prioritize conducting thorough code reviews and adhering to best practices in programming. I leverage automated testing, maintain comprehensive documentation, and encourage team discussions on coding standards. Continuous integration and deployment practices also help to catch issues early and guarantee high-quality standards.

My approach to troubleshooting involves a systematic methodology. I begin by reproducing the issue and collecting logs or metrics to understand its context. I often collaborate with team members to brainstorm potential causes and solutions. Utilizing monitoring tools and analyzing system performance is essential in identifying the root of the issue quickly.

I'm particularly excited about the growing integration of AI and machine learning in big data analytics. The advancements in real-time data processing capabilities and the increasing use of cloud technologies also offer incredible opportunities for improvements in data scalability and performance. I'm eager to leverage these trends in our solutions at BigID.

In my previous roles, I have utilized Elastic technologies to enhance search capabilities and optimize data retrieval across applications. I can discuss specific use cases where I implemented ElasticSearch effectively, focused on indexing strategies, and tuned performance settings to handle large datasets efficiently.

I prioritize clarity in communication, ensuring that requirements from cross-functional teams are translated effectively into technical specifications. I leverage collaboration tools and regularly update stakeholders on progress. Holding meetings focused on bridging gaps and addressing concerns helps to foster a cohesive working environment.

Data modeling and ETL processes have been core components in my previous positions. I focus on designing efficient models that reflect business requirements and optimize data flow. I have hands-on experience in developing ETL pipelines that ensure data quality and integrity, always aiming for automation where possible to improve efficiency.

I stay current with big data technology by participating in online courses, attending industry conferences, and engaging with professional communities. I subscribe to leading tech publications and follow influential figures in the field. This helps me to understand emerging trends and apply innovative solutions in my work.

Collaboration is essential for a Senior Data Platform Engineer because it enables the synthesis of diverse ideas and expertise, leading to innovative solutions. It fosters a culture of shared responsibility for the final product's success, ensures that all requirements are considered, and results in a more resilient system overall.

CI/CD principles play a pivotal role in my software development process by promoting frequent code integration and automated testing. This minimizes integration problems and allows for the quick delivery of features and fixes. It also provides a feedback loop that helps maintain high-quality standards and speeds up development.