Manager - Data Engineer

Role Summary

This role will serve dual responsibilities:

1. Schema Steward (Metadata Owner) to maintain authoritative metadata standards, IDs, naming conventions, and data definitions across the Data Lake.
2. Data Engineer to build, validate, and maintain extraction and file-drop pipelines from systems such as TDMS and PDM, ensuring high-quality, structured, and consistent data ingestion.

The role ensures metadata consistency, technical feasibility, and smooth execution of integration activities across all phases.

Key Responsibilities

1. Metadata Ownership (Schema Steward)

• Own and continuously update the Metadata Dictionary for all R&D domains.
• Define and standardize unique identifiers such as Variant_ID, Compound_ID, DOE_ID, Test_ID, etc.
• Maintain naming conventions, versioning rules, and schema evolution practices.
• Validate sample metadata records to ensure adherence to standards.
• Ensure metadata consistency across all tools, systems, dashboards, and analytical use cases.
• Collaborate with IT Architect and functional SMEs to maintain a robust metadata governance framework.

2. Data Engineering Responsibilities

• Support R&D tools data extraction, cleansing, normalization, and ingestion processes.
• Design and assist with structured file-drop or automated ingestion pipelines for Data Lake loading.
• Perform feasibility validation for Phase-2 integration candidates, assessing data availability, structure, volume, and integration complexity.
• Conduct data discovery and profiling to understand source system nuances.
• Collaborate with the Architect to ensure data models comply with designed contracts and metadata rules.
• Implement pipeline monitoring, error handling, and data quality checks.

Required Skills & Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 5–8 years of experience in data engineering, metadata governance, or related roles.
• Strong expertise in ETL/ELT tools, file-based integrations, and data transformation logic.
• Prior experience R&D data or laboratory/engineering data sources is a strong plus.
• Understanding of metadata frameworks, data dictionaries, and data governance processes.
• Ability to work across cross-functional teams and ensure strict adherence to data standards.