Software Data Engineer II

As a Software Data Engineer II at Helpshift, your primary responsibilities will include designing, building, and maintaining data pipelines for ingestion and analytics, collaborating with stakeholders to understand their data needs, and creating customer-facing analytics products. You'll also be required to document design specifications and mentor team members, ensuring the delivery of accurate and reliable data in a fast-paced environment.

To qualify for the Software Data Engineer II role at Helpshift, candidates should have a Bachelor’s Degree in Computer Science or a related field, along with at least 3 years of experience in building scalable data pipelines. Proficiency in programming languages such as Python or Java, as well as experience with cloud data platforms like Snowflake and AWS, are essential. Familiarity with data processing engines like Apache Spark and experience in data modeling and warehousing are also important.

A Software Data Engineer II at Helpshift needs a solid foundation in high-level object-oriented programming languages, particularly Python or Java. Additionally, expertise in cloud platforms like Snowflake and AWS, distributed data processing frameworks like Apache Spark, and data modeling practices is essential. Knowledge of workflow orchestration tools and data visualization platforms will also be beneficial.

As a Software Data Engineer II, your contributions are vital to Helpshift’s mission of improving customer service. By developing data pipelines that facilitate analytics and insights, you help brands understand and enhance their customer interactions. Your work supports real-time responses to user inquiries, thereby improving overall service delivery and customer satisfaction.

Helpshift offers a range of benefits to its Software Data Engineer II employees, including a hybrid work setup, workers' insurance, paid time off, and various other employee benefits that are detailed by the Talent Acquisition team. We value our team members and provide a supportive work environment with opportunities for professional growth and career advancement.

When designing a data pipeline for terabytes of data, I would first assess the data's source, format, and required processing. I would then use scalable cloud solutions, such as AWS or Snowflake, to store and process the data efficiently. Implementing distributed processing frameworks like Apache Spark would allow for parallel processing, minimizing latency. Additionally, I’d ensure regular monitoring and logging of the pipeline's performance to detect any bottlenecks.

I have extensive experience with SQL, including writing complex queries for data retrieval, optimization, and profiling. My background includes work in data warehousing environments where I’ve designed star and snowflake schema models to ensure data is structured for efficient querying. I've also optimized query performance by indexing and partitioning tables.

To ensure the reliability and accuracy of data in my pipelines, I implement rigorous testing protocols, including unit testing and end-to-end testing, to identify any issues before production. Monitoring data quality metrics is also crucial, alongside establishing alerting systems for anomalies. Furthermore, regular audits and reviews of data sources and processes ensure ongoing accuracy.

In a previous role, I encountered an issue where a data ingestion pipeline was lagging due to unexpected data volume surges. To address this, I optimized the existing workflow by implementing asynchronous processing, which reduced the load on the system. Additionally, I scaled out the processing components using AWS resources to efficiently handle the increased volume, ultimately stabilizing the pipeline.

Yes, I have utilized various data visualization tools such as Tableau and Power BI to create dashboards that present data insights effectively. I focus on making the visualizations intuitive and accessible for stakeholders, allowing them to derive actionable insights from complex datasets.

I prefer using Python for data engineering tasks due to its versatility and extensive library support for data manipulation and processing. Its readability and community support make it a great choice for building maintainable data pipelines. However, I also appreciate Java for its performance capabilities in large-scale systems.

I have hands-on experience with distributed data processing engines, particularly Apache Spark. I’ve utilized Spark for batch processing as well as streaming data applications, allowing for real-time data analysis. This experience has taught me how to leverage these engines to efficiently handle large datasets while optimizing resource allocation.

I stay updated on trends and technologies in data engineering by subscribing to relevant tech blogs, participating in online forums, and attending industry conferences. I also engage in continuous learning through online courses focusing on emerging tools like data lakehouses and new frameworks, ensuring that my skills remain current and relevant.

My experience with cloud data platforms, particularly AWS and Snowflake, has involved setting up data warehouses and implementing ETL processes. Additionally, I have utilized these platforms for data storage and analytics, benefitting from their scalability and performance capabilities to handle large volumes of data effectively.

To mentor a junior data engineer, I would adopt hands-on teaching, guiding them through real-world projects to enhance their practical skills. I’d conduct regular knowledge-sharing sessions, encouraging them to ask questions and challenge themselves. Providing constructive feedback and fostering a supportive learning environment would be fundamental to their development.