Data Analytics Engineer

EXL; Hyderabad, Pune District, Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

EXL in Hyderabad is seeking an experienced Data Analytics Engineer to design, build, and optimize scalable data pipelines and analytics infrastructure for critical financial products.

You will work at the intersection of software engineering and data analytics, building reliable ETL/ELT pipelines, orchestrating cloud-native workflows, and ensuring high-quality data for reporting, risk, and product teams in a regulated environment.

Qualifications

• 5+ years of experience in data engineering or analytics engineering.
• Experience delivering production-grade data pipelines in a cloud environment.
• Awareness of regulatory and data-security requirements in financial services.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines across cloud/on‑prem sources.
• Develop Python/PySpark and SQL transformations for large financial datasets.
• Architect and manage data pipeline orchestration (Airflow, Databricks Workflows, Step Functions).
• Build and maintain CI/CD pipelines using GitHub/GitHub Actions.
• Develop cloud-based solutions on AWS (S3, Glue, EMR, Redshift, Lambda, IAM).
• Deploy infrastructure and pipelines as code with versioning and monitoring.
• 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 (PII, SOX, PCI).
• Document architecture, data models, and processes; contribute to engineering standards.

Skills

Python

PySpark

SQL

AWS

Airflow

Databricks

GitHub Actions

Data Modeling

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Airflow

Databricks

GitHub Actions

Job description

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.