Senior Data Engineer
This Senior Data Engineer role focuses on architecting and optimizing end-to-end data solutions for enterprise clients, bridging Blend360’s internal teams with stakeholders to deliver scalable analytics and operational workflows across hybrid environments. The position emphasizes hands-on implementation of modern data tools while ensuring reliable, high-performance data pipelines for decision-making.
Skills required
About this role
We’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.
Qualifications
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.
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.
