Data Analytics Engineer

We are 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, high-quality data for reporting, risk, and product teams. This role requires strong engineering discipline (version control, CI/CD, infrastructure-as-code) combined with deep SQL and PySpark expertise, ideally within a regulated banking or financial services environment.

EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world's leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.

EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL's Human Resources team, as well as our hiring managers.

• Required Technical Skills

• Advanced proficiency in Python for scripting, automation, and data engineering workflows.

• Strong hands-on experience with PySpark for distributed data processing at scale.

• Expert-level SQL and Advanced SQL (window functions, query optimization, complex joins, performance tuning).

• Solid experience with AWS cloud services and cloud-based application/data development (S3, Glue, EMR, Redshift, Lambda, IAM, CloudWatch).

• Proven expertise building and orchestrating data pipelines (Airflow, Databricks Workflows, Step Functions, or equivalent).

• Hands-on CI/CD experience using GitHub / GitHub Actions for automated build, test, and deployment.

• Deep understanding of ETL/ELT design patterns, data modeling, and data warehousing concepts.

• Experience deploying infrastructure and pipelines via code (e.g. version-controlled deployments).

• Demonstrated ability to optimize pipeline performance, query execution, and cloud resource/cost efficiency.

Preferred / Desired Skills (Nice to Have)

• Hands-on experience with Databricks (Delta Lake, Unity Catalog, notebooks, cluster optimization).
• Familiarity with Terraform or CloudFormation for infrastructure as code.
• Experience with streaming data technologies (Kafka, Kinesis, Spark Structured Streaming).
• Exposure to data quality/testing frameworks (Great Expectations, Dbt tests).
• Knowledge of Dbt for transformation and analytics engineering workflows.
• Understanding of financial data domains - payments, lending, risk, fraud, or accounting data.
• Relevant certifications (AWS Certified Data Analytics/Solutions Architect, Databricks Certified Data Engineer).

Qualifications

• Bachelor's degree in computer science, Engineering, Data Science, or a related field (or equivalent practical experience).

• 5+ years of experience in data engineering, analytics engineering, or a related technical role.

• Prior experience working within banking, fintech, or financial services, with awareness of regulatory and data-security requirements.

• Demonstrated track record delivering production-grade data pipelines in a cloud environment.

• Soft Skills

• Strong analytical and problem-solving skills with attention to detail and data accuracy.

• Excellent communication skills; able to translate technical concepts for non-technical stakeholders.

• Collaborative mindset with experience working cross-functionally with analysts, engineers, and business teams.

• Self-directed and comfortable owning projects end-to-end in a fast-paced, regulated environment.

Strong ownership mentality around data quality, reliability, and documentation.

Base Compensation Range: $140,000- $155,000

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.

• Design, build, and maintain scalable, reliable ETL/ELT data pipelines across cloud and on-prem sources, ensuring data quality, lineage, and auditability.
• Develop and optimize Python/ PySpark and SQL-based data transformations for large-scale, high-volume financial datasets.
• Architect and manage data pipeline orchestration (e.g., Airflow, Databricks Workflows, Step Functions) to automate ingestion, transformation, and delivery.
• Build and maintain CI/CD pipelines using GitHub/GitHub Actions to support automated testing, deployment, and version-controlled infrastructure changes.
• Develop cloud-based solutions on AWS (S3, Glue, EMR, Redshift, Lambda, IAM) supporting analytics, reporting, and downstream ML use cases.
• Deploy and manage infrastructure and pipelines as code, following best practices for environment promotion, rollback, and monitoring.
• Monitor, troubleshoot, and optimize pipeline performance, query efficiency, and cost across the data stack.
• Partner with data scientists, analysts, product, and risk/compliance teams to translate business requirements into robust data solutions.
• Enforce data governance, security, and regulatory compliance standards appropriate for financial data (PII, SOX, PCI, etc.).
• Document pipeline architecture, data models, and processes; contribute to engineering standards and code review practices.
• Design, build, and maintain scalable, reliable ETL/ELT data pipelines across cloud and on-prem sources, ensuring data quality, lineage, and auditability.
• Develop and optimize Python/ PySpark and SQL-based data transformations for large-scale, high-volume financial datasets.
• Architect and manage data pipeline orchestration (e.g., Airflow, Databricks Workflows, Step Functions) to automate ingestion, transformation, and delivery.
• Build and maintain CI/CD pipelines using GitHub/GitHub Actions to support automated testing, deployment, and version-controlled infrastructure changes.
• Develop cloud-based solutions on AWS (S3, Glue, EMR, Redshift, Lambda, IAM) supporting analytics, reporting, and downstream ML use cases.
• Deploy and manage infrastructure and pipelines as code, following best practices for environment promotion, rollback, and monitoring.
• Monitor, troubleshoot, and optimize pipeline performance, query efficiency, and cost across the data stack.
• Partner with data scientists, analysts, product, and risk/compliance teams to translate business requirements into robust data solutions.
• Enforce data governance, security, and regulatory compliance standards appropriate for financial data (PII, SOX, PCI, etc.).
• Document pipeline architecture, data models, and processes; contribute to engineering standards and code review practices.