Senior ETL Developer (Part-time)

As a Senior ETL Developer at Faptic Technology, your primary responsibilities include designing, developing, and maintaining ETL/ELT pipelines using tools like Azure Data Factory and Synapse. You will build and optimize data models, develop integration solutions, and ensure data integrity while collaborating with cross-functional teams to translate business needs into scalable data solutions. Additionally, you’ll leverage Power BI for reporting and utilize Azure DevOps for version control.

To qualify for the Senior ETL Developer position at Faptic Technology, candidates must possess a Bachelor's degree in Computer Science, Engineering, or related fields, along with 5+ years of ETL development experience. Expertise in Microsoft Azure environments and certifications like Azure Data Engineer Associate or Power BI Data Analyst Associate is essential, as is proficiency in SQL, Python, and REST APIs.

A Senior ETL Developer at Faptic Technology should excel in ETL development and possess strong knowledge of Microsoft Azure services. Key skills include SQL proficiency, understanding of data pipeline principles, experience with Graph databases, and excellent problem-solving abilities. Strong communication skills and the capacity to independently manage multiple projects are also crucial for success.

In your role as a Senior ETL Developer at Faptic Technology, you will primarily use tools such as Azure Data Factory, SQL Server, Azure Synapse, and Power BI. You'll also employ Azure DevOps for version control and CI/CD implementation, while developing and optimizing data models and pipelines to ensure efficient processing and analytics.

As a Senior ETL Developer at Faptic Technology, your work will directly impact the company’s mission by designing and implementing data solutions that enhance customer experiences and optimize internal processes. Your contributions toward building data pipelines and ensuring data integrity will help the organization deliver exceptional technical solutions, fostering a culture of innovation and excellence.

When discussing your ETL process, emphasize the stages of extraction, transformation, and loading you implement. Mention how you gather requirements, identify data sources, apply transformations using tools like Azure Data Factory, and ensure data quality throughout the process. Illustrate your approach to troubleshooting and optimizing performance.

In optimizing ETL performance, focus on strategies such as partitioning data, utilizing parallel processing, and tuning SQL queries. Discuss how you benchmark the performance of your ETL jobs regularly and make adjustments as necessary to improve efficiency, along with your use of Azure tools to monitor performance.

When answering this, provide a specific example where you encountered obstacles, detailing your analytical approach and the steps you took to resolve the issues. Highlight your ability to communicate effectively with stakeholders, gather requirements, and deliver scalable solutions despite challenges.

To maintain data integrity, discuss practices like implementing validation rules during transformation, conducting regular audits on the data post-load, and methods for error-handling. Highlight any automated testing frameworks you’ve implemented to catch discrepancies early in the ETL cycle.

While discussing your experience with data models, describe the types you've worked with and the importance of normalizing data to prevent redundancy. Talk about tools or languages you use for designing, and any specific methodologies that guide your design processes, like star schema or snowflake schema design.

Describe your experience with Azure DevOps in the context of version controlling your ETL solutions, implementing CI/CD practices, and automating deployment processes. Emphasize how this familiarity has helped you collaborate better within teams and streamline development workflows.

In tackling inconsistent data from an API, emphasize your approach to validating inputs and ensuring data cleaning is a priority. Discuss how you would handle discrepancies by implementing fallback mechanisms or error logging strategies to capture when data does not meet expected standards.

Highlight the importance of documentation in your ETL projects. It's essential for maintaining records of data flow, transformations, and architecture decisions. Good documentation aids in onboarding new team members and provides clarity on data processes for future developments.

Discuss how ETL involves extracting data, transforming it before loading into the target destination, while ELT loads the raw data first and transforms it later. Mention scenarios when one might be preferred over the other, typically based on project size and complexity or technology stack.

Discuss your approach to continuous learning, like following industry blogs, participating in forums, engaging in online courses, or attending conferences. Share any specific resources or communities that help you stay abreast of new tools, techniques, and best practices in the ETL and data integration space.