Job Description

We are looking for a Lead Data Engineer to build and maintain scalable data pipelines and data platforms that support analytics, business Intelligence, reporting, and AI. In this hands-on role, you will work closely with data architects, business stakeholders and analysts to develop reliable data solutions and ensure high-quality data is available across the organization. For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

Salary range: $130k-$170k plus benefits

Lead Data Engineering (Los Angeles)

Job Functions:

• Collaborate with client stakeholders to gather requirements, structure solutions, and ensure high‑quality, timely delivery.
• Experience working in the Databricks tech stack with strong proficiency in SQL, Python, and PySpark
• Design and optimize data models, data marts, and Lakehouse/warehouse layers with strong focus on medallion architecture, query optimization, and performance engineering.
• Build, orchestrate, and monitor scalable data pipelines on Databricks, ensuring reliable ingestion, transformation, CDC handling, and incremental load strategies.
• Manage end‑to‑end pipeline operations including performance tuning, data quality monitoring, alerting, and issue resolution across production workloads.
• Lead a project team of data engineers supporting multiple workstreams and provide technical leadership through code reviews, best‑practice guidance, reusable pattern creation, and mentorship to engineering team members.
• Prepare and maintain project documentation to support project execution and delivery.

Expected work split

• 50% Technical – Data modeling, hands-on coding, orchestration, and pipeline monitoring.
• 50% Management– Client Collaboration, requirements gathering, designing technical solutions, presentations, global team management and mentoring.

Qualifications (Required):

• 8-12 years’ experience in data engineering and analytics roles
• Bachelor’s or Master's degree in analytics, computer science/engineering, economics, mathematics, or related areas.
• Experience building and maintaining ETL/ELT pipelines
• Solid understanding of data warehousing concepts and dimensional data modeling
• Familiarity with workflow orchestration tools such as Airflow or similar
• Experience working with cloud data platforms or modern data infrastructure
• Entrepreneurial hands-on approach to work. Demonstrated leadership ability and willingness to take initiative
• Superior analytical and problem solving skills
• Outstanding written and verbal communication skills
• Effective time management and attention to detail
• Hands on experience in using SQL, Python and Workflow Schedulers (Apache Airflow, Cron)
• Experience in leading team and coordinating with internal / external stakeholders
• Experience in using Cloud Platforms (AWS / GCP / Azure)
• Experience in using Visualization tools (Tableau / Power BI)
• Experience with Big Data Technologies (Hadoop, Hive, Hbase, Pig, Spark, etc.)

• Collaborate with client stakeholders to gather requirements, structure solutions, and ensure high‑quality, timely delivery.
• Experience working in the Databricks tech stack with strong proficiency in SQL, Python, and PySpark
• Design and optimize data models, data marts, and Lakehouse/warehouse layers with strong focus on medallion architecture, query optimization, and performance engineering.
• Build, orchestrate, and monitor scalable data pipelines on Databricks, ensuring reliable ingestion, transformation, CDC handling, and incremental load strategies.
• Manage end‑to‑end pipeline operations including performance tuning, data quality monitoring, alerting, and issue resolution across production workloads.
• Lead a project team of data engineers supporting multiple workstreams and provide technical leadership through code reviews, best‑practice guidance, reusable pattern creation, and mentorship to engineering team members.
• Prepare and maintain project documentation to support project execution and delivery.

Work Split

• 50% Technical – Data modeling, hands-on coding, orchestration, and pipeline monitoring.
• 50% Management– Client Collaboration, requirements gathering, designing technical solutions, presentations, global team management and mentoring.