Senior RAID Developer

As a Senior RAID Developer at Advansys, you will be primarily responsible for developing and maintaining efficient SQL-based solutions, designing scalable data processing systems with Apache Spark and Hive DB, and building optimized data pipelines using Python. Your role also includes collaborating with cross-functional teams, performing performance optimization, and ensuring adherence to coding best practices, making it a critical position that impacts multiple aspects of our data handling capabilities.

To be considered for the Senior RAID Developer role at Advansys, candidates should have a strong background in SQL and PL/SQL, as well as experience with Hive DB and Apache Spark. Proficiency in Python programming is essential, along with a familiarity with big data technologies and data warehousing concepts. Effective communication skills are also a must to collaborate effectively with various teams and stakeholders.

As a Senior RAID Developer at Advansys, you'll work with a variety of advanced technologies, primarily SQL and PL/SQL for data solutions, Apache Spark for data processing, and Hive DB for managing large datasets. You'll also employ Python for building data pipelines and might work with additional big data technologies such as Hadoop and Kafka, broadening your skill set in the data tech landscape.

The Senior RAID Developer role at Advansys is crucial for driving our mission to deliver smart and sustainable technology solutions. By optimizing data systems and ensuring efficient data handling, you will enhance operations and improve customer experiences, supporting our goal of business modernization and helping enterprise customers effectively utilize their data.

At Advansys, the work environment for a Senior RAID Developer is dynamic and collaborative. You will be part of a skilled team of over 400 engineers across 14 countries, contributing to diverse and challenging projects. Emphasis is placed on innovation and teamwork, allowing you to engage deeply with cross-functional teams while working on impactful data solutions.

To optimize a complex SQL query, I would analyze the execution plan to identify bottlenecks, avoid using 'SELECT *' statements, ensure that indexes are appropriately used, and consider breaking down the query into smaller parts if necessary. It's important to always test the performance before and after to ensure that changes yield the desired effect.

I have extensive experience with Apache Spark that includes designing data processing workflows, optimizing Spark jobs for performance, and leveraging its capabilities for batch and streaming data processing. I'm familiar with key concepts like RDDs and DataFrames and have used Spark to handle large datasets, ensuring data accuracy and scalability.

Data integrity and accuracy are paramount in my work. I ensure this by implementing data validation rules, performing regular audits, and developing robust ETL processes that include error handling mechanisms. Collaborating with data quality teams and utilizing automated tools helps maintain high standards throughout the data lifecycle.

In my data manipulation and ETL processes, I commonly use libraries such as Pandas for data analysis, NumPy for numerical processing, and Dask for handling larger-than-memory computations. Additionally, I leverage SQLAlchemy for database interactions and PySpark when working specifically with Apache Spark.

I have utilized Hive DB extensively for managing and querying large datasets. My experience includes writing HiveQL queries to extract meaningful insights, tuning performance through partitioning and bucketing, and integrating Hive with other data processing frameworks like Spark to optimize data flow and reporting.

When encountering performance issues in data pipelines, I systematically investigate the pipeline to identify bottlenecks. This includes reviewing data transformation steps, optimizing queries, and enhancing parallel processing. I often implement monitoring solutions to track performance metrics, allowing for proactive adjustments.

In a previous role, I collaborated with data scientists and business analysts to define requirements for a new data platform. This involvement included facilitating workshops to gather insights and translating business needs into technical specifications, ensuring that the final solution served the company's goals effectively.

I have hands-on experience with data warehousing concepts, including design, ETL processes, and data modeling. My experience involves working with star and snowflake schemas, ensuring data is structured for optimal querying and reporting, and using tools like AWS Redshift or Snowflake to implement the warehousing solutions.

I ensure coding best practices by adhering to established coding standards, utilizing version control systems, and performing code reviews with peers. I also emphasize documentation for ongoing maintenance and clarity in data processes, along with writing unit tests to validate functionality and robustness.

One challenging project involved migrating a legacy data system to a modern cloud-based solution. This required meticulous planning, creating a comprehensive ETL strategy while ensuring minimal downtime. I successfully led a team that transformed data processes, improving performance and accessibility significantly, which streamlined our analytics capabilities.