Experienced Data Analyst For Manufacturing Systems
Budget: $15 – $25 USD
We are seeking a Data Analyst / Engineer with substantial experience in manufacturing control systems data, data authentication, and readiness engineering. This role will ensure data ingested from manufacturing systems into the Client's data lake and downstream applications (e.g., PI) is fit-for-purpose, reliable, and trustworthy.
The successful candidate will work directly with manufacturing system owners, the CDL data lake team, and business stakeholders to resolve data integrity issues surfaced during the Trailblazer deployment.
Key Responsibilities
Analyze, validate, and authenticate data sets from manufacturing systems (SCADA, PLC, control systems).
Identify gaps, missing values, and integrity issues in structured and semi-structured data.
Implement data readiness and authentication protocols to ensure “fit-for-purpose” ingestion.
Develop automation scripts, pipelines, and data preparation workflows to streamline readiness processes.
Collaborate with CDL ingestion teams to fix and enrich data before and after ingestion into the CDL.
Work with stakeholders to define data requirements per application, e.g., PI datasets.
Provide technical support to parallel Trailblazer rollouts across multiple manufacturing sites.
Create and maintain documentation for data readiness processes, authentication standards, and audit protocols.
Contribute to building audit controls and monitoring frameworks for ongoing data integrity.
Required Skills & Experience
6–10 years’ experience as a Data Analyst / Engineer in manufacturing or industrial data environments.
Strong understanding of SCADA, PLC, and manufacturing control systems data.
Experience with data lakes (Azure/AWS/GCP) and relational database systems.
Hands-on experience in data authentication, cleansing, validation, and readiness engineering.
Proficiency in SQL, Python, or equivalent scripting for data processing/automation.
Familiarity with PI (OSIsoft / Aveva) or other manufacturing data historians (trainable by Deen).
Strong problem-solving skills for working with ambiguous or incomplete data sets.
Ability to collaborate with both technical and functional (system owners, BSA) stakeholders.
Prior experience with data process automation in manufacturing systems.
Exposure to audit/compliance controls in pharma or life sciences manufacturing.
Working knowledge of data visualization and reporting tools.
Familiarity with Agile/PI planning environments.
The successful candidate will work directly with manufacturing system owners, the CDL data lake team, and business stakeholders to resolve data integrity issues surfaced during the Trailblazer deployment.
Key Responsibilities
Analyze, validate, and authenticate data sets from manufacturing systems (SCADA, PLC, control systems).
Identify gaps, missing values, and integrity issues in structured and semi-structured data.
Implement data readiness and authentication protocols to ensure “fit-for-purpose” ingestion.
Develop automation scripts, pipelines, and data preparation workflows to streamline readiness processes.
Collaborate with CDL ingestion teams to fix and enrich data before and after ingestion into the CDL.
Work with stakeholders to define data requirements per application, e.g., PI datasets.
Provide technical support to parallel Trailblazer rollouts across multiple manufacturing sites.
Create and maintain documentation for data readiness processes, authentication standards, and audit protocols.
Contribute to building audit controls and monitoring frameworks for ongoing data integrity.
Required Skills & Experience
6–10 years’ experience as a Data Analyst / Engineer in manufacturing or industrial data environments.
Strong understanding of SCADA, PLC, and manufacturing control systems data.
Experience with data lakes (Azure/AWS/GCP) and relational database systems.
Hands-on experience in data authentication, cleansing, validation, and readiness engineering.
Proficiency in SQL, Python, or equivalent scripting for data processing/automation.
Familiarity with PI (OSIsoft / Aveva) or other manufacturing data historians (trainable by Deen).
Strong problem-solving skills for working with ambiguous or incomplete data sets.
Ability to collaborate with both technical and functional (system owners, BSA) stakeholders.
Prior experience with data process automation in manufacturing systems.
Exposure to audit/compliance controls in pharma or life sciences manufacturing.
Working knowledge of data visualization and reporting tools.
Familiarity with Agile/PI planning environments.
Related categories:
Python
Data Processing
SQL
Azure
Data Cleansing
Data Analysis
Relational Databases
PLC