Data Engineering Manager

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 Data Engineering Manager to contribute to our next level of growth and expansion.

What is this position about? We're looking for a Data Engineering Manager to lead the design and delivery of complex data pipelines and integration solutions that power critical business workflows for our clients. You will own end-to-end data engineering initiatives from discovery and POC development through production deployment and optimization, while building and scaling a high-performing engineering team. Your work will span platform-agnostic data architecture, high-volume record matching, deduplication, and near-real-time delivery systems. You will guide teams through complex data modeling decisions, performance optimization, and integration design, helping clients understand the tradeoffs between batch and real-time approaches, API-driven versus clean-room workflows, and infrastructure options.

Design and implement data ingestion architectures on Snowflake. Lead data engineering projects end to end—from discovery and requirements through POC validation to production deployment—while building and mentoring a high-performing engineering team Design platform-agnostic data pipelines and schemas that scale reliably at high volume, including complex data modeling for submit-grade-return workflows, identity graphs, record matching, and deduplication systems Own discovery pipeline initiatives and functional POCs, implementing high-volume matching logic and conducting comprehensive performance, load, and scale testing to validate architectural decisions Assess and optimize latency, throughput, connection stability, and integration options including APIs, batch delivery, and governed clean-room workflows Define data integration strategies and communicate tradeoffs clearly to clients, guiding infrastructure and tool selection based on performance requirements and business constraints Lead partner-facing API design and integration strategy, architecting batch versus real-time decisions and designing governed clean-room solutions for partner data exchange Establish technical standards for schema design, SQL quality, pipeline development, and production readiness across the team through reviews, mentoring, and knowledge sharing Qualifications What We Are Looking For 7+ years of hands-on data engineering experience, with proven expertise in building and deploying high-volume data pipelines in production 2+ years of direct team leadership or technical management experience Advanced SQL proficiency with deep expertise in schema and data-model design, complex joins, and performance optimization Proven track record with high-volume record matching, deduplication, and identity resolution systems Strong experience with platform-agnostic data engineering—ability to assess and implement solutions across Snowflake, Databricks, BigQuery, Redshift, and other platforms Demonstrated expertise in pipeline development, performance testing, latency and throughput analysis Experience designing and implementing batch and real-time data integration workflows Knowledge of governed clean-room solutions and data governance practices Solid understanding of API design, microservices patterns, and event-driven architectures A