Data Analyst

 

EXL is recognized as a global leader in the fields of analytics, artificial intelligence, and digital solutions, catering to a diverse range of industries. As you may know, this company specializes in digital transformation and is publicly traded on NASDAQ under the ticker symbol EXLS. With a market valuation of approximately $1.3 billion, EXL boasts a workforce of over 48,000 employees spread across the globe. The company proudly serves more than 800 clients worldwide, demonstrating its extensive reach and influence in the market.

DATA ANALYTICS ENGINEER
Fintech / Financial Services / Full-Time / 5+ Years Experience

Role Summary
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.

Key Responsibilities
• 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.

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.