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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 is the indispensable partner for leading businesses in data-led industries such as insurance, banking and financial services, healthcare, retail and logistics. We bring a unique combination of data, advanced analytics, digital technology and industry expertise to help our clients turn data into insights, streamline operations, improve customer experience, and transform their business. Our partnerships with clients are built on a foundation of collaboration - and we've been chosen as a partner by nine of the top ten leading US insurance companies, nine of the top 20 global banks, and six of the top ten US health care payers. We function as one team to make your goals our goals, whether that's unlocking the value of generative AI or embedding analytics into workflows that reduce risk or power your growth. Clients choose EXL as their transformation partner for many reasons. Our geographic diversity make talent all over the world instantly accessible. Digital accelerators enable unmatched speed-to-value, letting you realize results fast. It's our people that truly set us apart, though, including the 1,500 data scientists we have dedicated to our generative AI practice. And our more than twenty years of experience in delivering business services, garnering stellar client references, and maintaining a solid balance sheet are reassuring to our C-suite clients. Find out for yourself why clients, employees, and analysts think we're some of the best in the business. Contact us to see how we can help you achieve your goals.

• 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.

• 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.

• 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).

• 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.

• 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.

• 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.