Data Analytics Engineer: Cloud Pipelines for Finance

EXL; San Francisco (CA); On-site; USD 140,000 - 160,000; Full time

Job summary

EXL is seeking an experienced Data Analytics Engineer to design, build, and optimize scalable data pipelines and analytics infrastructure that power critical financial products and decisions.

You will work at the intersection of software engineering and data analytics, building reliable ETL/ELT pipelines, orchestrating cloud-native workflows, and enabling trusted data for reporting, risk, and product teams.

Qualifications

• Bachelor's degree in computer science, engineering, data science, or related field.
• 5+ years of data engineering/analytics engineering experience.
• Experience in banking/financial services with regulatory awareness.

Responsibilities

• Design and maintain scalable ETL/ELT pipelines.
• Develop Python/ PySpark and SQL-based transformations for large financial datasets.
• Architect data pipeline orchestration (Airflow, Databricks Workflows, Step Functions).
• Build CI/CD pipelines with GitHub/GitHub Actions for automated testing and deployment.
• Develop cloud-based solutions on AWS (S3, Glue, EMR, Redshift, Lambda, IAM).
• Manage infrastructure and pipelines as code with versioned deployments.
• Monitor and optimize pipeline performance, cost, and reliability.
• Collaborate with data scientists, analysts, product, and risk/compliance teams.
• Enforce data governance, security, and regulatory compliance.

Skills

Python

PySpark

SQL

AWS

Airflow

Databricks

CI/CD

ETL/ELT

Data modeling

Data governance

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

GitHub Actions

Terraform

CloudFormation

Job description

EXL is seeking an experienced Data Analytics Engineer to design, build, and optimize scalable data pipelines and analytics infrastructure that power critical financial products and decisions.

You will work at the intersection of software engineering and data analytics, building reliable ETL/ELT pipelines, orchestrating cloud-native workflows, and enabling trusted data for reporting, risk, and product teams.