We are seeking a highly motivated, forward thinking Lead Data Engineer to support the enterprise Collaboration and Support Center Manufacturing excellence group and the GP Decision Analytics Group. The role is focused on developing and automating data analytics solutions used across 150+ facilities within multiple divisions. GP CSC and the Decision Analytics Group functions as a Center of Excellence for all things related to manufacturing and commercial transformation for all of Georgia Pacific. This groups create sustainable value and competitive advantage by leveraging analytics, information technology, and actionable insights across the enterprise while focusing on futuristic possibilities of analytics. This role will have the opportunity to leverage the latest Big Data, Cloud and Analytics technologies and partner with our operations, engineering and data science community to expand our modeling and decision-making capabilities focused on asset health, asset optimization and process optimization.
Job Responsibilities
- Hands-on lead for data engineering in the area of Quality and Operations for the Enterprise data and analytics team focused on managing the Data Lake and helping the business develop, deploy and manage predictive and prescriptive models to create business value through optimization of manufacturing facilities.
- Develop critical data pipelines and data quality jobs in the AWS environment working with Lambda, Glue, Python, SQL and noSQL databases.
- Enhance and optimize exiting data quality processes including automated testing and alerting on critical data assets.
- Assist Data Science teams with preparing, cleansing, and delivering analytical datasets for machine learning models.
- Monitor SAS and Python based models in production from a model quality perspective.
- Participate in various engineering projects building data product tools utilizing batch and streaming data.
- On-call support rotation, troubleshooting and improving existing Operations processes including logging, alerting, and monitoring.
- Manage own learning and contribute to technical skill building of the team.
- Develop deep technical expertise in the data movement patterns, practices and tools.
- Demonstrate technical, team, and solution leadership through strong communication skills to recommend actionable, data-driven solutions
- Collaborate with team members, business stakeholders and data SMEs to elicit requirements and to develop a technical design and then implement a solution.
Basic Qualifications: - Bachelor's degree in Engineering (preferably Analytics, MIS or Computer Science). Master's degrees preferred.
- Minimum 5 years of Data Engineering experience and 2-3 years working with AWS serverless technologies like AWS Lambda, AWS Glue, Kinesis, DynamoDB and Redshift.
- At least 2 years of Python development experience. At least 2 years experience with using relational databases and SQL.
- Data Concepts knowledge (ETL, near-/real-time streaming, data structures, data modeling, metadata, and workflow management).
- Experience in Big Data projects utilizing EMR, Kinesis, Spark.
- Proficiency in databases, SQL, and data warehousing concepts.
- A passion and fearlessness for learning new technologies and putting them in practice.
- Ability to thrive in a dynamic team environment.
- Excellent written and verbal communications skills.
- Ability to draw technical diagrams and present to the team.
Preferred Qualifications: - AWS certifications
- Manufacturing IT systems knowledge (OSI PI)
- Markup Languages (JSON, XML, YAML)
- Code Management Tools (Git/GitHub, Bit Bucket, SVN, TFS)
- SAS tools and scripting experience.
- Experience with DevOps processes and tools like Terraform.
- Ability to pull together complex and disparate data sources, warehouse those data sources and architect a foundation to produce BI and analytical content, while operating in a fluid, rapidly changing data environment.
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Last updated on Dec 28, 2022