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
Job DescriptionWe are looking for an experienced Manager – Data Engineering with 8+ years of experience in building and managing data engineering solutions in an on-premise environment. The ideal candidate should have strong hands-on expertise in Python, Apache Spark, SQL, and Apache Airflow, along with proven experience in leading data engineering teams and delivering scalable data pipelines.
Key Responsibilities
- Lead and manage a team of Data Engineers and provide technical guidance and mentorship.
- Design, develop, and optimize scalable data pipelines in an on-premise environment.
- Build robust ETL/ELT workflows using Python, Spark, SQL, and Airflow.
- Design and manage complex Apache Airflow DAGs for data pipeline orchestration.
- Develop and optimize Spark-based data processing solutions for large datasets.
- Write complex and optimized SQL queries for data extraction, transformation, and analysis.
- Troubleshoot pipeline failures, performance issues, and data quality challenges.
- Work closely with architects, business stakeholders, and cross-functional teams to understand requirements and deliver solutions.
- Conduct code reviews and ensure adherence to engineering and development best practices.
- Drive technical design, estimation, planning, and end-to-end project delivery.
- Monitor team performance, project timelines, risks, and dependencies.
- Mentor engineers and contribute to building a strong data engineering practice.
Qualifications
- 8+ years of overall experience in Data Engineering.
- Strong hands-on experience with Python.
- Strong experience with Apache Spark / PySpark.
- Advanced SQL skills, including complex joins, CTEs, window functions, subqueries, and query optimization.
- Strong experience with Apache Airflow for workflow orchestration and scheduling.
- Experience working with on-premise data environments.
- Strong understanding of ETL/ELT processes and data pipeline architecture.
- Experience handling large volumes of data and optimizing data processing workloads.
- Good understanding of data quality, monitoring, troubleshooting, and performance optimization.
Leadership Requirements
- Proven experience managing or leading Data Engineering teams.
- Strong stakeholder and client management skills.
- Ability to provide technical direction while managing project delivery.
- Experience with resource planning, task allocation, mentoring, and performance management.
- Strong communication and problem-solving skills.
- Ability to work in a fast-paced, Agile environment.
Good to Have
- Experience in large-scale enterprise data platforms.
- Experience with data warehouse concepts and data modelling.
- Experience with on-premise Hadoop/data ecosystems.
- Experience in migrating or modernizing legacy/on-premise data platforms.
Skills Required
- 8+ years of experience in Data Engineering
- Strong hands-on experience with Python
- Strong experience with Apache Spark / PySpark
- Advanced SQL skills including complex joins, CTEs, window functions, and query optimization
- Strong experience with Apache Airflow for workflow orchestration
- Experience designing, developing, and optimizing scalable data pipelines in an on-premise environment
- Experience building robust ETL/ELT workflows
- Proven experience managing or leading Data Engineering teams
- Experience troubleshooting pipeline failures, performance issues, and data quality challenges
- Strong stakeholder management and communication skills; experience in Agile environments
- Experience with large-scale enterprise data platforms
- Experience with data warehouse concepts and data modeling
- Experience with on-premise Hadoop/data ecosystems
- Experience migrating or modernizing legacy/on-premise data platforms
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
-
Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
-
Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
-
Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
