Lead Data Engineer

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: $93,900 - $154,200 Base annually

The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

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):

• 6-8 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.