Data Engineer

As a Data Engineer at Envision Employment Solutions, you'll be responsible for designing, developing, and maintaining data pipelines that support vital data integration and processing needs. You'll ensure data quality and security across all systems and develop efficient ETL processes. Collaboration with data scientists and analysts is essential, as you'll provide them the data necessary for analytics and reporting.

To apply for the Data Engineer position at Envision Employment Solutions, you should hold a bachelor’s degree in computer science, Information Technology, Data Science, or a related field. Additionally, having at least 5+ years of experience focusing on building and maintaining data pipelines is essential, as well as proficiency in Azure environments, SQL, and big data technologies.

A Data Engineer plays a crucial role in enabling informed decision-making at Envision Employment Solutions by ensuring that high-quality and timely data is accessible to various stakeholders. By designing robust data pipelines and maintaining the integrity of data systems, you will support analysts and decision-makers in deriving insights that guide organizational strategies.

For a Data Engineer at Envision Employment Solutions, proficiency in programming languages such as Python, Java, and SQL is essential. These skills empower you to effectively develop and implement data processing solutions and tackle complex data-related challenges.

At Envision Employment Solutions, a Data Engineer should adhere to robust data governance practices by ensuring compliance with data protection regulations, maintaining data quality and integrity, and implementing best practices across all data processes. It's also important to provide training and support to stakeholders.

When designing a data pipeline, you should first define the data sources and the types of data that will be processed. Next, establish conditions for data transformation and develop ETL processes to ensure efficient data integration. Finally, implement monitoring tools to track performance and troubleshoot as necessary.

Ensuring data quality involves implementing validation techniques during the data ingestion process and continuously monitoring data sets for accuracy and consistency. Regular audits and checks at various stages of the pipeline also help maintain high data quality.

I have utilized Azure Data Factory for orchestrating data workflows, and I have built data transformation solutions using Azure Databricks. These platforms help me manage data more effectively within cloud environments, ensuring scalability and reliability.

To troubleshoot data pipeline issues, I start by reviewing error logs and metrics to identify the root cause of the problem. I also employ testing strategies, simulate data flows, and collaborate with team members to find and resolve issues promptly.

In a past project, I led a data integration effort that involved consolidating datasets from multiple sources into a single warehouse. I implemented an ETL process that reduced data processing time by 30%, significantly improving accessibility for the analytics team.

I am well-versed in data protection regulations such as GDPR and HIPAA. Understanding these regulations is vital for ensuring compliance when handling sensitive data throughout our data engineering processes.

I manage competing priorities by effectively planning my day, prioritizing tasks based on urgency and importance, and maintaining open communication with my team and stakeholders to align expectations and deliverables.

I have extensive experience with big data technologies such as Hadoop and Spark, which I’ve used to handle large volumes of data, perform batch and real-time processing, and facilitate analytics operations. These skills are crucial for driving insights in data-intensive projects.

I use various strategies, including data partitioning, indexing, and compression techniques to optimize data storage and retrieval. Additionally, I assess and tune performance to ensure that retrieval times meet or exceed business expectations.

I stay current with emerging data technologies by subscribing to industry newsletters, participating in online forums, attending webinars and conferences, and engaging in continuous learning through courses and certifications.