YOUR SKILLS & ABILITIES (REQUIRED QUALIFICATIONS)

• 5 + years’ experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field or equivalent experience
• Strong and hands-on experience in databricks data engineering and management.
• Experience with setting up CI/CD environment in Azure and Databricks leveraging Azure Devops, ARM templates, GitHub actions and DBX Asset bundles and Terraform etc.
• Strong understanding of Gen AI tools and proven ability to integrate AI tools such as Microsoft co-pilot, data bricks genie with daily tasks in Databricks.
• Experience with big data frameworks and tools like Apache Hadoop, Apache Spark, or Apache Kafka for processing and analyzing large datasets.
• Strong understanding and ability to provide mentorship in the areas of data ETL processes and tools for designing and managing data pipelines.
• Experience in designing streaming data pipelines using FiveTran, Azure Event Hubs, Auto Loader and delta lake in Azure Databricks.
• Hands on experience with data serialization formats like JSON, Parquet, YML and XML.
• Background in designing data solutions that are highly automated and provide consistent and accurate outcomes.
• Hands on experience in orchestrating automated workflows or batch jobs on Azure Databricks platform.
• Understanding of data governance principles, data privacy regulations, and experience with implementing security measures for data protection.
• Ability to work effectively in cross-functional teams, collaborate with data scientists, analysts, and stakeholders to deliver data solutions.
• At least 3 years of hands-on experience with Big Data Tools: Hadoop, Spark, Kafka, etc.
• Ability to identify tasks which require automation and automate them
• A demonstrable understanding of networking/distributed computing environment concepts
• Ability to multi-task and stay organized in a dynamic work environment

WHAT CAN GIVE YOU A COMPETITIVE ADVANTAGE (PREFERRED QUALIFICATIONS)

• Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
• AWS cloud services: EC2, EMR, RDS, Redshift
  This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.

Compensation:

• The expected base compensation for this role is: $94,800 - $159,600. Actual base compensation within the identified range will vary based on factors relevant to the position.
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
• Benefits : GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

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THE ROLE

As a Data Engineer, you will build industrialized data assets and optimize data pipelines in support of Business Intelligence and Advance Analytic objectives. You will work closely with our forward-thinking Data Scientists, BI Developers, System Architects and Data Architects to deliver value to our vision for the future. Are you ready to join a future facing team?

WHAT YOU’LL DO

• Communicate and maintain Master Data, Metadata, Data Management Repositories, Logical Data Models, Data Standards
• Create and maintain optimal data pipeline architecture
• Assemble large, complex data sets that meet functional / non-functional business requirements
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build industrialized analytic datasets and delivery mechanisms that utilize the data pipeline to deliver actionable insights into customer acquisition, operational efficiency and other key business performance metrics
• Work with business partners on data-related technical issues and develop requirements to support their data infrastructure needs
• Ability to create highly consistent and accurate analytic datasets suitable for business intelligence and data scientist team members