Data Engineer III

As a Data Engineer III at our Lewisville, TX service center, your primary responsibilities include creating and maintaining optimal data pipeline architecture, assembling complex data sets to meet business requirements, and identifying internal process improvements. You'll also build infrastructure for data extraction, transformation, and loading, as well as analytics tools to derive insights from data.

To qualify for the Data Engineer III role in Lewisville, TX, candidates should possess a Bachelor’s degree in Computer Science, Statistics, or a related field, along with 4-6 years of experience in data engineering. Advanced knowledge of SQL, experience with ETL processes, and familiarity with both SQL and NoSQL databases are essential.

As a Data Engineer III in Lewisville, TX, familiarity with Google Cloud Platform, Snowflake, and data pipeline management tools is vital. Additionally, experience with message queuing and stream processing technologies like Pub-Sub and Kafka will greatly enhance your ability to succeed in this role.

The Data Engineer III role at our Lewisville, TX service center is crucial for aligning data systems with business goals. By ensuring optimal data delivery and creating complex analytics tools, you help drive insights that improve customer acquisition and operational efficiency, ultimately supporting the organization's strategic initiatives.

Success in the Data Engineer III role at our Lewisville, TX service center relies on strong analytical skills, collaboration with cross-functional teams, and a proactive approach to solving data-related challenges. A history of manipulating large datasets and experience in machine learning can also be significant asset.

When building a data pipeline, I start by gathering requirements to understand the data sources and formats needed. I then design the architecture, considering scalability and efficiency. After that, I implement the extraction, transformation, and loading (ETL) processes, testing each step to handle potential errors before final deployment.

Ensuring data quality is crucial, and I typically employ validations at each stage of the data pipeline. This includes verifying data types, checking for duplicates, and implementing checks on data integrity to catch issues early, improving the reliability of the data being processed.

I have extensive experience with SQL, having created complex queries to extract and manage data in various projects. For instance, I've developed stored procedures and views that helped streamline reporting processes, ultimately leading to quicker and more accurate business insights.

I automated a data extraction process by utilizing SSIS and Azure Data Factory. By creating scheduled tasks, I was able to minimize manual intervention, which not only saved time but also reduced errors significantly, allowing the team to focus on more strategic tasks.

When dealing with unstructured datasets, I start by performing exploratory analysis to understand the data's nature and structure. I then utilize Python for preprocessing tasks such as text extraction and transformation, making it easier to convert this data into a structured format suitable for analysis.

Documentation is essential in data engineering as it ensures that processes are clear and maintainable by other team members. I document the architecture, data flows, and any decisions made to provide context and facilitate smoother transitions for future updates or troubleshooting.

I once faced a challenge with slow data retrieval times in a critical pipeline. I resolved it by optimizing the query performance, indexing tables, and restructuring the pipeline to improve efficiency significantly, resulting in faster data availability for reporting.

I stay updated on trends in data engineering by following industry blogs, participating in online forums, and attending conferences. Additionally, I dedicate time to learning new tools and technologies that can enhance my skill set and improve my contributions to my team.

I have been involved in multiple data migration projects, where I've transferred data between different systems. This experience taught me the importance of thorough planning and testing to ensure data integrity and the smooth transition of data without disruptions to ongoing operations.

Cloud technologies are integral to data engineering as they provide flexibility, scalability, and efficient resource management. I have utilized platforms like Google Cloud to execute data pipelines, leveraging services like BigQuery for analytics, which significantly enhances overall data handling capabilities.