Data Engineer Industrial and Refinery Data📣 إعلان

منذ يوم واحد

	نوع العقد	دوام كامل
	طبيعة الوظيفة	بالموقع
	الموقع	الرياض

وصف الوظيفة

About Tata Consultancy Services

Tata Consultancy Services (TCS) is an IT services, consulting, and business solutions organization with over 50 years of experience partnering with global businesses in their transformation journeys. TCS offers a consulting-led, cognitive-powered, integrated portfolio of business, technology, and engineering services and solutions. This is delivered through its Location Independent Agile™ delivery model, recognized for excellence in software development. As part of the Tata group, India's largest multinational business group, TCS employs over 616,171 consultants from 157 nationalities across 53 countries.

The Role: Data Engineer - Industrial and Refinery Data

TCS is seeking a Data Engineer specializing in industrial and refinery data to join its team in Riyadh, Saudi Arabia. This full-time role focuses on developing and maintaining robust data pipelines for high-volume structured and time-series data, crucial for industrial operations.

Key Responsibilities

Building reliable data pipelines designed to handle high-volume structured and time-series data.
Integrating various data sources, including historians, laboratory, maintenance, asset, and engineering data.
Applying strong understanding of data quality, metadata, lineage, security, and access controls in all data engineering activities.

Required Competencies

Experience in building reliable data pipelines for high-volume structured and time-series data.
Hands-on knowledge of SQL, Python, APIs, data modeling, and ETL/ELT frameworks.
Experience integrating historians, laboratory, maintenance, asset, and engineering data sources.
Strong understanding of data quality, metadata, lineage, security, and access controls.
Familiarity with feature stores, vector databases, or AI-ready data platforms.

Preferred Competencies

Experience with data in Oil & Gas, refinery, or process manufacturing sectors.
Exposure to OSI PI or equivalent historians, LIMS, CMMS/SAP, and streaming platforms.
Ability to contextualize tag-level data against equipment, process hierarchy, and engineering information.