Senior Data Engineer

As a Senior Data Engineer at GROW Inc, you will be responsible for designing and building a scalable data platform, creating data pipelines, and empowering internal teams to self-service their data needs. Your role includes writing clean code, conducting code reviews, and solving complex technical challenges. Additionally, you will mentor less experienced engineers, contribute to automated testing, and improve our data quality framework.

To qualify for the Senior Data Engineer role at GROW Inc, you should have over 6 years of experience in designing, building, deploying, and scaling data platforms. Extensive knowledge of cloud architectures, stream and batch processing, relational and NoSQL databases, as well as fluency in SQL, Python, and Java, are essential. Experience with Databricks PySpark, Docker, and cloud services like AWS, GCP or Azure is also highly desired.

GROW Inc is committed to fostering the growth of its employees. As a Senior Data Engineer, you will have opportunities to mentor less experienced engineers and lead in technical practices and best practices. We encourage continuous learning, experimentation, and collaboration across teams to enhance both personal and team development.

The culture at GROW Inc is inclusive, diverse, and vibrant, embracing different perspectives and backgrounds to drive innovation. As a Senior Data Engineer, you will be joining a team that values authenticity and encourages everyone to bring their unique selves to work. We celebrate curiosity, accountability, and a collaborative spirit, creating an environment where you can thrive.

As a Senior Data Engineer at GROW Inc, you will enjoy benefits such as birthday leave, a weekly pay cycle, and a true hybrid work arrangement. Additionally, we offer exciting discounts, including on movie tickets and electronics, to make your experience even more rewarding. Join us to explore these fantastic perks and more!

When designing a scalable data platform, I start by understanding the specific needs of the business and users. I prioritize clean architecture, modularity, and robustness in my design. Additionally, I leverage best practices like using microservices and containerization, ensuring the platform is flexible for future growth.

In my previous projects, I have designed and implemented various solutions on AWS and GCP. I understand the critical components of cloud architecture including cost-effectiveness, scalability, and security, which are fundamental in a role like Senior Data Engineer. I ensure my design incorporates best practices for cloud deployment and resiliency.

I once faced a performance bottleneck in a data pipeline due to inefficient queries. To resolve this, I analyzed the query execution plan and optimized it by refactoring the SQL and leveraging partitions in the database. This improved the data processing time significantly, illustrating the importance of deep analysis and iterative testing.

To ensure data quality, I employ a combination of validation rules, automated testing, and monitoring tools. I establish data quality metrics early in the process and integrate them into the CI/CD pipeline, allowing for continuous checks throughout the data lifecycle.

I've implemented several data warehousing solutions, utilizing ETL processes to transform and load data into optimized storage. I am familiar with star and snowflake schema design, and I understand how to leverage tools like dbt to build robust data models for analytical purposes.

I tend to adopt a hands-on approach when mentoring junior engineers. I emphasize encouraging questions and guiding them through real-world problems. This promotes not only technical skills but also confidence and independence in their problem-solving abilities.

I find tools like Prometheus and Grafana to be immensely helpful for monitoring. They provide visualizations that can help identify performance issues quickly. I ensure that proper logging and alerts are set up to proactively manage the health of our data pipelines.

When faced with tight deadlines, I prioritize tasks based on their impact and urgency. I effectively communicate with stakeholders to set realistic expectations and focus on delivering quality work without compromising standards. Team collaboration is also key to meet deadlines successfully.

I have extensive experience using Databricks with Apache Spark for large-scale data processing. I've utilized SparkSQL for querying data and built data pipelines using PySpark, which allow for parallel processing, enhancing performance and scalability in analytics tasks.

I am particularly drawn to GROW Inc's mission to innovate within the Superannuation industry using cutting-edge technology. The emphasis on collaborative teamwork and diversity aligns well with my values, and I am excited about the opportunity to contribute to meaningful projects that impact the financial wellbeing of Australians.