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Senior Data Engineer
1 week ago
As a Data Design Engineer for Manufacturing, you will play a crucial role in transforming raw manufacturing data into actionable insights. Your responsibilities will include:
- Collaborating with stakeholders to design scalable, efficient data architectures tailored for manufacturing processes.
- Developing and maintaining data models, schemas, and structures to support key manufacturing metrics such as yield, cycle times, SPC, trending, and process interdependencies.
- Ensuring data architecture aligns with industry best practices and supports unique manufacturing process needs.
- Optimizing data storage and retrieval, considering the volume and velocity of manufacturing data.
- Integrating manufacturing data seamlessly into the overall data architecture by working closely with cross-functional teams.
- Managing system integrations between Manufacturing, OT, and Enterprise IT environments (e.g., PLC to MES, MES to OKTA, ERP, PLM).
- Designing, implementing, and optimizing ETL (Extract, Transform, Load) processes for reliable, scalable, and high-performance data pipelines.
- Ensuring consistency, accuracy, and reliability in data integration from various systems.
- Analyzing and interpreting key process indicators to drive process optimization and improvement.
- Implementing and enforcing data quality standards and governance best practices.
- Managing and optimizing database systems for data availability, performance, and security.
- Creating compelling visualizations of manufacturing data using tools such as Tableau and Power BI.
Qualifications:
- Bachelor’s degree in Computer Science, Information Technology, or a related field.
- Minimum 8 years in IT, with 5+ years in Data Engineering, preferably in manufacturing support.
- Strong programming skills in Python, Java, or Scala.
- Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (e.g., AWS, Azure, GCP).
- Proficiency in SQL and database management systems (e.g., MS-SQL, Oracle, Postgres).
- Familiarity with data lake/warehousing concepts and tools.
- Excellent problem-solving, analytical, communication, and collaboration skills.
- Preferred: Master’s degree, industry certifications, experience with containerization/orchestration (e.g., Docker, Kubernetes), and knowledge of machine learning concepts.
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