About the role

Data Analytics Engineer

Experience: 5+ Years
Location: Gurugram, Noida, Pune, Hyderabad, Chennai, Bangalore
Work mode: Hybrid

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, orchestratingcloud-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 andcode 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, performancetuning).
• 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.