Description

Own end-to-end data engineering delivery, balancing hands-on technical execution with team leadership and client stakeholder management.

This role is on-site.

Responsibilities

• Design and optimize data models, medallion architectures, and Lakehouse layers using SQL, Python, and PySpark.
• Build, orchestrate, and monitor scalable ETL/ELT pipelines on Databricks, ensuring reliable ingestion, CDC handling, and incremental loads.
• Lead a team of data engineers through code reviews, best practice guidance, and mentorship across multiple workstreams.
• Collaborate with client stakeholders to gather requirements, structure solutions, and manage global team coordination.
• Perform performance tuning, data quality monitoring, alerting, and issue resolution for production workloads.

Required Skills

• 8-12 years of experience in data engineering and analytics roles.
• Strong proficiency in SQL, Python, and PySpark.
• Experience building and maintaining ETL/ELT pipelines with a focus on dimensional data modeling.
• Hands-on experience with workflow schedulers such as Apache Airflow or Cron.
• Demonstrated leadership ability with experience managing internal and external stakeholders.
• Bachelor’s or Master's degree in analytics, computer science, engineering, economics, mathematics, or related fields.
• Solid understanding of data warehousing concepts and modern data infrastructure.

Preferred Skills

• Experience with cloud platforms (AWS, GCP, or Azure).
• Familiarity with visualization tools like Tableau or Power BI.
• Background with Big Data technologies such as Hadoop, Hive, or Spark.

Key Skills

Education

Any Graduate