Data Engineer

Blend is seeking a skilled Data Engineer with substantial experience in distributed systems, Python/Scala, and contemporary data engineering tools. The role involves designing and implementing a comprehensive end-to-end data architecture for a prominent enterprise client, supporting a strategic initiative to enable advanced analytics, BI, and operational workflows across both on-premise and cloud environments. In this position, you will collaborate closely with Blend's internal teams and client stakeholders to construct and optimize data pipelines, assist with data modeling, and ensure the reliability of data flows for analytics, reporting, and informed business decision-making. This opportunity is ideal for engineers with a solid data foundation eager to apply their skills in large-scale, modern data environments and gain exposure to sophisticated architectures and tools.

Responsibilities

• Design and implement end-to-end data solution architectures tailored to enterprise analytics and operational requirements.
• Build, maintain, and optimize data pipelines and transformations utilizing Python, SQL, and Spark.
• Manage large-scale data storage and processing using technologies such as Iceberg, Hadoop, and HDFS.
• Develop and maintain dbt models to ensure data quality, reliability, and structure.
• Implement robust data ingestion processes, integrating with third-party APIs and on-premise systems.
• Collaborate with cross-functional teams to align on business objectives and technical specifications.
• Contribute to documentation and continuously enhance engineering and data quality processes.

Qualifications

• A minimum of 3 years of experience in data engineering, with at least 1 year focused on on-premise systems.
• Proficiency in Python for data workflow and pipeline development.
• Strong experience with SQL, Spark, Iceberg, Hadoop, HDFS, and dbt.
• Familiarity with third-party APIs and data ingestion methodologies.
• Excellent communication and interpersonal skills, with the ability to work autonomously and engage effectively with both technical and non-technical stakeholders.