Job Description

Location: Boston, MA
Position Type: Hybrid
Hybrid Schedule: 4 days/week onsite
Contract Length: 7 months, contract-to-hire


This role involves designing, building, and optimizing modern data platforms that power intelligent, data-driven experiences for global clients. Responsibilities include enabling scalable ingestion, transformation, and storage of enterprise data across lakehouse and warehouse architectures while operating at the intersection of cloud, data engineering, and analytics. The position also includes close collaboration with architects, analysts, and product teams to ensure data solutions are reliable, high-performing, and aligned with business objectives.

Duties:

• Design and implement end-to-end data ingestion pipelines using Azure services, including API-based ingestion and Azure Data Factory (ADF).
• Build and manage lakehouse and data warehouse solutions using modern data storage formats to support analytical and operational workloads.
• Develop and optimize data transformations using PySpark, ensuring scalability, performance, and cost efficiency.
• Apply medallion architecture (bronze, silver, gold layers) to enable high-quality, governed, and reusable datasets.
• Partner with cross-functional teams to support data modeling, analytics, and downstream consumption use cases.
• Contribute to best practices around data quality, reliability, and maintainability across the data platform.

Required Qualifications:

• Hands-on experience or strong working knowledge of Microsoft Fabric, including its role in modern analytics and lakehouse architectures.
• Proven experience working in Azure for data ingestion and orchestration.
• Strong experience with Azure Data Factory (ADF) for pipeline development and scheduling.
• Experience building API-based data ingestion solutions.
• Solid understanding of data storage formats, including CSV, JSON, and Parquet.
• Experience designing and working with data warehouses and lakehouse architectures.
• Strong foundation in data modeling concepts for analytical workloads.
• Practical experience implementing medallion architecture patterns.
• Proficiency in PySpark for large-scale data transformations and optimization.
• Ability to write clean, maintainable, and well-documented data pipelines.

Preferred Qualifications:

• Experience optimizing Spark jobs for performance and cost in cloud environments.
• Familiarity with data governance, data quality, or observability practices in large-scale data platforms.
• Experience collaborating with analytics, data science, or AI teams on production-grade data solutions.
• Exposure to agile delivery models and working in cross-functional, client-facing teams.