Lead / Manager - Data Engineering

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 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.

7+ 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.

• 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.

7+ 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.