Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com
We are seeking a to contribute to our next level of growth and expansion.
Job DescriptionWe’re looking for a skilled Senior Data Engineer with strong experience in distributed systems, Python/Scala, and modern data engineering tools to help design and implement an end-to-end data architecture for a leading enterprise client. This role is part of a strategic initiative to enable robust analytics, BI, and operational workflows across both on-premise and cloud environments.
In this role, you’ll work closely with both Blend’s internal teams and client stakeholders to build and optimize data pipelines, support data modeling efforts, and ensure reliable data flows for analytics, reporting, and business decision-making.
This position is ideal for engineers with a strong data foundation who are looking to apply their skills in large-scale, modern data environments, while gaining exposure to advanced architectures and tooling.
You will:
Design and implement an end-to-end data solution architecture tailored to enterprise analytics and operational needs.
Build, maintain, and optimize data pipelines and transformations using Python, SQL, and Spark.
Manage large-scale data storage and processing with Iceberg, Hadoop, and HDFS.
Develop and maintain dbt models to ensure clean, reliable, and well-structured data.
Implement robust data ingestion processes, integrating with third-party APIs and on-premise systems.
Collaborate with cross-functional teams to align on business goals and technical requirements.
Contribute to documentation and continuously improve engineering and data quality processes.
4+ years of experience in data engineering, including at least 1 year with on-premise systems.
Proficiency in Python for data workflows and pipeline development.
Strong experience with SQL, Spark, Iceberg, Hadoop, HDFS, and dbt.
Familiarity with third-party APIs and data ingestion processes.
Excellent communication skills, with the ability to work independently and engage effectively with both technical and non-technical stakeholders.
Preferred Qualifications
Master’s degree in Data Science or a related field.
Experience with Airflow or other orchestration tools.
Hands-on experience with Cursor, Copilot, or similar AI-powered developer tools.
