Data, Engineer

About the Role

Master Works is seeking a highly skilled Data Engineer to design, develop, and maintain a robust, scalable, and secure data platform. This role is pivotal in enabling advanced analytics, data science initiatives, and downstream applications by building foundational data infrastructure, developer tooling, and self-service capabilities. The ideal candidate will collaborate with cross-functional teams to deliver high-performance data solutions that empower stakeholders to derive actionable insights efficiently and securely.

Key Responsibilities

The Data Engineer will be responsible for the following core functions:

• Data Platform Development: Design, construct, and maintain a scalable data platform that supports analytics, machine learning, and real-time applications. Ensure the platform is secure, reliable, and optimized for performance.
• Self-Service Data Solutions: Develop reusable, self-service data pipelines and platform services that allow internal teams to access and manipulate data with minimal friction while adhering to governance and security standards.
• Data Orchestration & Monitoring: Implement and maintain data orchestration workflows, observability tools, and monitoring systems to track pipeline performance, data quality, and system reliability. Proactively identify and resolve bottlenecks or failures.
• Data Quality & Integrity: Establish and enforce rigorous data quality standards, ensuring accuracy, consistency, and reliability across all systems. Implement validation frameworks and monitoring to detect anomalies and discrepancies.
• Architectural Leadership: Define and enforce data platform standards, best practices, and architectural patterns to promote consistency, scalability, and maintainability. Collaborate with engineering and product teams to align data infrastructure with business objectives.
• Data Integration & Transformation: Integrate data from diverse sources, including APIs, databases, streaming systems, and third-party platforms. Transform raw data into structured, usable formats to support analytics and reporting.
• Storage Optimization: Manage and optimize data storage solutions, including data warehouses, data lakes, and Lakehouse architectures, to balance cost, performance, and accessibility.
• Cross-Functional Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet their needs. Translate business requirements into technical specifications and data models.
• Security & Compliance: Implement and maintain robust data security measures, governance frameworks, and compliance protocols to protect sensitive information and ensure adherence to regulatory requirements.
• Documentation & Knowledge Sharing: Document data architecture, pipeline designs, workflows, and processes to ensure transparency and facilitate knowledge transfer across teams. Maintain up-to-date technical documentation for all data systems and tools.

Requirements

To excel in this role, candidates must meet the following qualifications:

• Education: Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related quantitative field. Advanced degrees or certifications in data engineering or related disciplines are a plus.
• Technical Expertise: Proven experience designing, building, and operating large-scale data platforms or systems. Hands-on expertise in developing data pipelines, ETL/ELT processes, and data transformation workflows.
• Programming & Querying: Advanced proficiency in SQL for complex data queries and transformations. Strong programming skills in Python, Java, or Scala, with a focus on writing clean, efficient, and maintainable code.
• ETL & Data Integration: Extensive experience with Informatica tools, including PowerCenter and Informatica Intelligent Cloud Services, or equivalent platforms. Ability to integrate and harmonize data from multiple sources while ensuring consistency and reliability.
• Orchestration & Workflow Management: Experience with data orchestration tools such as Airflow, Dagster, Prefect, or similar platforms to design, schedule, and monitor complex data workflows.
• Data Architecture Knowledge: Deep understanding of data lake, data warehouse, and Lakehouse architectures, including their respective strengths, use cases, and optimization strategies.
• Infrastructure as Code: Familiarity with infrastructure-as-code tools like Terraform to automate and manage data platform infrastructure deployments and configurations.
• Problem-Solving & Collaboration: Strong analytical and problem-solving skills with the ability to troubleshoot complex data issues. Excellent communication and collaboration abilities to work effectively with technical and non-technical stakeholders.

Work Location

This position is based at the client’s site, requiring on-site presence to collaborate with teams, participate in meetings, and ensure seamless integration with existing systems and workflows.