Data Engineer - (Remote - India)

As a Data Engineer at Jobgether, your main responsibilities will include owning and evolving the data architecture to ensure data quality and consistency. You will build scalable ETL/ELT pipelines using Databricks, integrate structured and unstructured data, and collaborate closely with cross-functional teams to meet their data needs.

To qualify for the Data Engineer position at Jobgether, you need a Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field. Additionally, you should have a minimum of 3 years of data engineering experience, with at least 1 year focused on Databricks, along with strong SQL and Python skills.

Jobgether leverages advanced technologies such as Databricks for ETL processes, Apache Spark, and Delta Lake, along with integration tools for CRM/ERP systems. Familiarity with cloud platforms like AWS, Azure, or GCP is also important for this role.

Yes, the Data Engineer role at Jobgether is fully remote, allowing you the flexibility to work from anywhere in India. We embrace modern work practices, which means you can balance professionalism with your personal lifestyle.

Jobgether offers competitive salary packages and abundant growth opportunities, along with access to continuous learning and professional development. You'll work in an innovative and collaborative environment that encourages your career advancement.

When designing data pipelines, I focus on understanding the data flow requirements from various sources, ensuring data quality and integrity are maintained. I also prioritize scalability and optimization, utilizing tools like Databricks to create efficient ETL processes.

I have worked extensively with Databricks for building scalable data pipelines, utilizing its features for data manipulation and transformation. I appreciate its integration with Apache Spark and the ease of managing large datasets within a collaborative workspace.

Key considerations for integrating data include ensuring compatibility of the data formats, maintaining data integrity during the transfer, and implementing security measures to protect sensitive information. Understanding the data structures of the source platforms is crucial.

To monitor data pipeline performance, I implement automated monitoring systems that track pipeline health and alert me to any anomalies. Regularly reviewing logs and conducting performance tuning based on usage patterns also forms a vital part of my approach.

I have utilized AWS and Azure for data storage and processing, leveraging services like S3 and Azure Data Lake to store large datasets. This experience has taught me how to optimize cloud resources effectively for data engineering tasks.

Ensuring data quality involves setting up validation checks at every stage of the ETL process, from extraction to loading. I also prioritize documentation and regularly audit the data pipeline to identify and rectify any discrepancies.

One challenge I faced was dealing with large volumes of unstructured data. I addressed this by implementing a preliminary data cleansing process and utilizing tools like Apache Spark to manage the data efficiently, improving overall processing speed.

I stay updated on the latest developments by subscribing to industry newsletters, attending webinars, and actively participating in data engineering forums. Continuous learning through online courses also helps me enhance my skill set.

I have worked on implementing data governance policies to ensure compliance with regulations like GDPR. This includes creating frameworks for data access, usage, and integrity, while also training team members on best practices.

When explaining complex data engineering concepts to non-technical stakeholders, I focus on using analogies and visuals to simplify the ideas. I prioritize understanding their requirements and tailoring my explanations to highlight how our solutions will benefit their objectives.