An exciting career awaits you

At MPC, we’re committed to being a great place to work – one that welcomes new ideas, encourages diverse perspectives, develops our people, and fosters a collaborative team environment.

As a Senior Data Engineer, you will design, develop, and optimize scalable data pipelines and cloud-based data solutions using modern data architecture, Azure data services, including Data Lake, Synapse Analytics, Azure Functions, and Databricks.

This role also includes close collaboration with analytics, product, architecture, governance, and business teams to translate requirements into scalable data solutions. The ideal candidate brings strong technical depth (strong proficiency in Python, SQL, Spark, and orchestration tools such as Azure Data Factory and/or Databricks Workflows), a continuous improvement mindset, and the ability to mentor while contributing to engineering standards, reusable patterns, and solution design reviews.

This position belongs to a family of jobs with increasing responsibility, competency, and skill level. Actual position title and pay grade will be based on the selected candidate’s experience and qualifications.

• Conducts the design, innovation and optimization of data extraction, ingestion and transformation processes.
• Facilitates the development and design of complex data architecture to process and store high-volume data sets.
• Enables the development of complex data pipelines; advocates for and implements data security and privacy measures.
• Conducts complex data quality and processing tasks using open source and cloud services.
• Provide technical expertise during critical incidents.
• Facilitates the adoption of best practices for data security and privacy and collaborates with other departments to ensure seamless data integration.
• Facilitates the implementation of continuous improvements in data processing methods and drives consistency and best practices across data engineering projects.
• Solves complex problems; takes a new perspective on existing solutions; participates in strategic planning sessions for data infrastructure.
• Oversee quality assurance and testing for data solutions.
• Mentors less experienced data engineers.

• Bachelor’s Degree in Information Technology, related field or equivalent experience
• 5+ years of relevant data engineering experience

• Big Data Technologies - Familiarity with big data technologies and frameworks, such as Hadoop, Spark, and distributed computing, for processing and analyzing large volumes of data.
• Cloud Platform - Knowledge of cloud-based data platforms, such as AWS, Azure, or GCP, and their associated services for data storage, processing, and analytics.
• Data Governance - Ability to establish and oversee a set of procedures, policies, and standards that ensure the effective and efficient management of an organization's data assets. This includes ensuring data quality, compliance with relevant laws and regulations, and secure data handling practices. It also involves the coordination between different departments to ensure that data is accurate, accessible, and used responsibly and ethically.
• Data Integration - Proficiency in integrating data from various sources, including structured and unstructured data, using technologies such as ETL (Extract, Transform, Load) processes, data pipelines, and data ingestion frameworks.
• Data Lifecycle - The data lifecycle refers to the sequential stages that data goes through from its creation or acquisition to its eventual disposal. These stages typically include data creation, storage, processing, analysis archival, and eventual deletion or destruction, with each phase governed by specific policies and practices.
• Data Modeling - Skill in designing and implementing data models that align with business requirements, ensuring data integrity, performance, and scalability.
• Data Operations - Data operations refer to the various actions and processes involved in managing, manipulating, and analyzing data throughout its lifecycle. These operations encompass tasks such as collection, storage, retrieval, transformation, and visualization of data to derive meaningful insights and support decision-making.
• Data Pipelines - Data pipelines are a set of processes that enable the flow of data from one or multiple sources to a destination, often involving tasks such as extraction, transformation, and loading (ETL). These pipelines are designed to efficiently and reliably move and process data, ensuring its quality and accessibility for various analytical and operational purposes.
• Data Privacy - Ability to understand and implement practices that ensure the protection and confidential handling of personal and sensitive information. This includes knowledge of relevant laws and regulations (such as GDPR or HIPAA), the ability to design and enforce policies that safeguard data, and the skills to manage data access rights and consent protocols.
• Data Quality Management – Strong understanding of data quality dimensions, methodologies, and best practices to establish and maintain data quality standards and processes.
• Data Security - Knowledge of data privacy regulations, cybersecurity best practices, and techniques for protecting sensitive information and ensuring compliance.
• Data Warehousing (DW) - Knowledge of monitoring and observability tools and practices for tracking data pipeline performance, data quality, and system health.
• DevOps- A set of practices that combines software development and information-technology operations which aims to shorten the systems development life cycle and provide continuous delivery with high software quality and a security first approach.
• General Programming – Applies a computer language to communicate with computers using a set of instructions and to automate the execution of tasks.
• Metadata Management – Proficiency in metadata management solutions to enable efficient data discovery, data lineage tracing, and data asset management.
• NoSQL Databases – NoSQL databases are a type of database management system that provides a flexible and scalable approach to storing and retrieving data, often diverging from the traditional relational database model. Unlike relational databases, NoSQL databases are designed to handle large volumes of unstructured or semi-structured data, offering high performance and horizontal scalability for modern applications.
• Real Time Processing – Real-time processing refers to the method of handling data or performing computations immediately as they occur, without any noticeable delay. In real-time processing systems, data is processed, and responses are generated within a timeframe that meets the requirements of the application or task, typically within milliseconds or microseconds.

MINIMUM QUALIFICATIONS:
Bachelor’s Degree in Information Technology, related field or equivalent experience.
5+ years of relevant experience