Data Engineer

at Credilio Financial Technologies Pvt. Ltd.

Data Engineer

Credilio Financial Technologies Pvt. Ltd.

Data Engineer at Credilio Financial Technologies Pvt. Ltd. · Mumbai · 3 - 8 years · Raised funding · Posted 30 Jul 2026

Data Engineer

at Credilio Financial Technologies Pvt. Ltd.

Posted by Munjal Dhamecha

3 - 8 yrs

Best in industry

Mumbai

Skills

Amazon Web Services (AWS)

PySpark

Data engineering

ClickHouse

We are looking for a hands-on Data Engineer to help build and manage our data platform for reporting, analytics, and future data science use cases.

The role will involve working with SQL, Python, PySpark, AWS, ETL/ELT pipelines, data warehouses, and BI/reporting tools. Our architecture may use technologies such as ClickHouse, Redshift, AWS Glue, Airflow, Step Functions, Lambda, S3, and CDC-based replication tools based on scale, cost, and operational needs.

Key Responsibilities

· Design, build, and maintain scalable ETL/ELT data pipelines from multiple databases, applications, and external systems.

· Build raw, cleaned, and business-ready data layers to support reporting, analytics, and future data science use cases.

· Write efficient SQL, Python, and PySpark jobs for data ingestion, transformation, validation, and processing.

· Implement workflow orchestration using Apache Airflow, AWS Glue, Step Functions, or similar tools.

· Work with data warehouses such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

· Support cross-service reporting as the architecture moves towards independent microservice databases.

· Build reusable reporting tables, aggregates, summaries, and basic data marts.

· Monitor, troubleshoot, and optimize data pipelines, warehouse queries, and processing jobs.

· Implement data quality checks for freshness, completeness, consistency, duplicates, and reconciliation.

· Support BI/reporting needs through tools such as Power BI, Metabase, Superset, Redash, or similar.

· Apply data governance, access control, security, and PII-handling best practices.

· Collaborate with engineering, DevOps, product, business, finance, risk, and support teams.

Required Skills

· Strong expertise in SQL for joins, aggregations, window functions, query optimization, and analytical reporting.

· Hands-on experience with Python for data processing, automation, validation, and scripting.

· Working experience with PySpark / Apache Spark for processing large datasets.

· Good understanding of ETL/ELT pipelines, data warehousing, and data modelling concepts.

· Experience with workflow orchestration using Apache Airflow, AWS Step Functions, AWS Glue, or similar tools.

· Experience with AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, DMS, CloudWatch, or similar.

· Experience with any data warehouse such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

· Understanding of relational databases, preferably PostgreSQL.

· Ability to debug data mismatches, failed pipelines, slow queries, and data quality issues.

· Exposure to BI tools such as Power BI, Metabase, Superset, Redash, or similar.

Good ownership, problem-solving, communication, and collaboration skills.

We are looking for a hands-on Data Engineer to help build and manage our data platform for reporting, analytics, and future data science use cases.

The role will involve working with SQL, Python, PySpark, AWS, ETL/ELT pipelines, data warehouses, and BI/reporting tools. Our architecture may use technologies such as ClickHouse, Redshift, AWS Glue, Airflow, Step Functions, Lambda, S3, and CDC-based replication tools based on scale, cost, and operational needs.

Key Responsibilities

· Design, build, and maintain scalable ETL/ELT data pipelines from multiple databases, applications, and external systems.

· Build raw, cleaned, and business-ready data layers to support reporting, analytics, and future data science use cases.

· Write efficient SQL, Python, and PySpark jobs for data ingestion, transformation, validation, and processing.

· Implement workflow orchestration using Apache Airflow, AWS Glue, Step Functions, or similar tools.

· Work with data warehouses such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

· Support cross-service reporting as the architecture moves towards independent microservice databases.

· Build reusable reporting tables, aggregates, summaries, and basic data marts.

· Monitor, troubleshoot, and optimize data pipelines, warehouse queries, and processing jobs.

· Implement data quality checks for freshness, completeness, consistency, duplicates, and reconciliation.

· Support BI/reporting needs through tools such as Power BI, Metabase, Superset, Redash, or similar.

· Apply data governance, access control, security, and PII-handling best practices.

· Collaborate with engineering, DevOps, product, business, finance, risk, and support teams.

Required Skills

· Strong expertise in SQL for joins, aggregations, window functions, query optimization, and analytical reporting.

· Hands-on experience with Python for data processing, automation, validation, and scripting.

· Working experience with PySpark / Apache Spark for processing large datasets.

· Good understanding of ETL/ELT pipelines, data warehousing, and data modelling concepts.

· Experience with workflow orchestration using Apache Airflow, AWS Step Functions, AWS Glue, or similar tools.

· Experience with AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, DMS, CloudWatch, or similar.

· Experience with any data warehouse such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

· Understanding of relational databases, preferably PostgreSQL.

· Ability to debug data mismatches, failed pipelines, slow queries, and data quality issues.

· Exposure to BI tools such as Power BI, Metabase, Superset, Redash, or similar.

Good ownership, problem-solving, communication, and collaboration skills.

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About Credilio Financial Technologies Pvt. Ltd.

Founded :

2020

Type :

Product

Size :

100-500

Stage :

Raised funding

About

Credilio is founded with the idea of digitizing the distribution value chain of personal finance products. The mission of Credilio is to create a nationwide army of multi-branded trained advisors who can assist in the digital onboarding of loans and cards. Credilio enables a small financial advisor to educate customers and recommend lending products that are best suited to meet their unique requirements and further enables them to subscribe for credit cards or loans digitally.

The company does this by providing a large range of product offerings and a customized recommendation tool through a simple mobile app that empowers advisors to be truly digital and future-ready. In addition, the platform allows for instant online approval by leveraging open banking APIs of Banks and NBFCs, thus making the purchase process transparent and paperless. It has reduced the average prospecting-to-onboarding time to less than 10 minutes in a single session as compared to the industry average of an interrupted process over 2-3 days.

A challenger start-up, Credilio is the brainchild of second-time entrepreneurial duo Mr. Aditya Gupta and Mr. Anand Kapadia. It’s backed by a clutch of Venture Capital funds namely Cornerstone Venture Capital Partners, Exfinity Venture Partners, and Param Capital founder Mukul Agarwal.

The platform currently partners with 20+ leading Banks and NBFCs and has over 10,000 Financial Advisors who have assisted more than 0.5 million customer applications getting processed fully digitally in a span of less than a year. The capital raised will power Credilio’s ambitious plans to meet an ARR of 100 cr. by March 2023 and serve 25 million customers through a million advisors in the next 3 years.

Credilio is founded with the idea of digitizing the distribution value chain of personal finance products. The mission of Credilio is to create a nationwide army of multi-branded trained advisors who can assist in the digital onboarding of loans and cards. Credilio enables a small financial advisor to educate customers and recommend lending products that are best suited to meet their unique requirements and further enables them to subscribe for credit cards or loans digitally.

The company does this by providing a large range of product offerings and a customized recommendation tool through a simple mobile app that empowers advisors to be truly digital and future-ready. In addition, the platform allows for instant online approval by leveraging open banking APIs of Banks and NBFCs, thus making the purchase process transparent and paperless. It has reduced the average prospecting-to-onboarding time to less than 10 minutes in a single session as compared to the industry average of an interrupted process over 2-3 days.

A challenger start-up, Credilio is the brainchild of second-time entrepreneurial duo Mr. Aditya Gupta and Mr. Anand Kapadia. It’s backed by a clutch of Venture Capital funds namely Cornerstone Venture Capital Partners, Exfinity Venture Partners, and Param Capital founder Mukul Agarwal.

The platform currently partners with 20+ leading Banks and NBFCs and has over 10,000 Financial Advisors who have assisted more than 0.5 million customer applications getting processed fully digitally in a span of less than a year. The capital raised will power Credilio’s ambitious plans to meet an ARR of 100 cr. by March 2023 and serve 25 million customers through a million advisors in the next 3 years.

Read more

Tech stack

NodeJS (Node.js)

TypeScript

AdonisJS

PostgreSQL

Flutter

Vue.js

Photos

Company featured pictures

Connect with the team

Munjal Dhamecha

Yusuf Qureshi

Company social profiles

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Support indexing, partitioning, storage optimization, and database performance.
Implement appropriate database security and access controls.
Support database monitoring, availability, backup/recovery, and operational requirements in collaboration with infrastructure/platform teams.
Troubleshoot database and pipeline performance issues.
Contribute to cloud data-platform configuration and optimization.

Data Quality

Implement automated validation and data-quality controls.
Develop reconciliation processes between source and target systems.
Identify duplicate, incomplete, inconsistent, and invalid data.
Implement cleansing, standardization, validation, and exception-management logic.
Work with technical and business stakeholders to understand and implement business data rules.

Technical Delivery

Convert technical designs into reliable production solutions.
Participate in technical design and code reviews.
Mentor junior/mid-level engineers.
Support estimation and technical planning.
Troubleshoot complex production and integration issues.
Collaborate with technical leads, application teams, DevOps, QA, BI, and client teams.
Document technical designs, mappings, configurations, operational procedures, and engineering decisions.

Consulting & AI-Led Working

Understand the business purpose behind assigned engineering requirements.
Raise risks, assumptions, dependencies, and potential improvements proactively.
Communicate effectively with client and internal stakeholders.
Use AI-assisted tools to improve coding, testing, analysis, documentation, and troubleshooting productivity.
Independently verify AI-generated code and recommendations before implementation.
Identify opportunities for engineering automation and process improvement.

Required Qualifications

6–9 years of experience in data engineering, database engineering, data integration, or related areas.
Advanced SQL skills.
Strong hands-on Python experience.
Strong ETL/ELT development experience.
Strong relational database knowledge, such as SQL Server, PostgreSQL, Oracle, or equivalent.
Experience with data modeling and database design.
Experience with AWS, Azure, or another major cloud platform.
Experience with modern data technologies such as Databricks, Snowflake, Redshift, Synapse, Glue, ADF, Airflow, dbt, or equivalent.
Experience with performance tuning and optimization.
Experience with data validation and reconciliation.
Strong troubleshooting and analytical skills.
Ability to take ownership of engineering deliverables with limited supervision.

Preferred Qualifications

Experience working with enterprise ERP, CRM, PIM, PLM, MES, or related systems.
Experience with manufacturing, retail, distribution, or consumer-goods data.
Experience with CI/CD and infrastructure/platform engineering practices.
Experience with API and event-driven integrations.
Previous consulting or technology-services experience.

What Success Looks Like

Pipelines and databases perform reliably at enterprise scale.
Engineering solutions are maintainable, reusable, and well documented.
Data issues are detected and addressed systematically.
Junior engineers receive effective technical guidance.
Problems are owned through resolution rather than simply escalated.
Engineering decisions reflect both technical requirements and client business objectives.

About Solutionara

Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications.

Headquartered in the United States with a Global Delivery Center (GDC) in Noida, India, Solutionara partners with enterprise organizations across retail, manufacturing, distribution, consumer goods, and other industries to design, implement, and support scalable technology solutions.

As a rapidly growing consulting organization, we foster a culture of ownership, innovation, collaboration, and continuous improvement, empowering our teams to contribute beyond their immediate responsibilities while delivering exceptional value to our global clients.

Every team member is expected to think like a consultant and business problem solver—understanding client objectives, challenging assumptions when appropriate, leveraging AI responsibly, and continuously identifying opportunities to improve quality, productivity, and customer outcomes.

AI is embedded into how we work, but it is an enabler—not a replacement for expertise, critical thinking, or accountability.

Role Overview

Solutionara is seeking a Senior Data Engineer with strong data engineering and database expertise to design, build, optimize, and support enterprise-scale data solutions.

This is a hands-on engineering position requiring strong SQL, Python, ETL/ELT, database, cloud, and troubleshooting capabilities. The engineer will work closely with technical leads and client stakeholders while taking ownership of major engineering components.

Location: Noida, India – Onsite | Employment Type: Full-Time | Experience: 6–9 Years

Key Responsibilities

Data Engineering

Design, develop, test, deploy, and support scalable ETL/ELT pipelines.
Build ingestion and transformation solutions across databases, APIs, files, and enterprise applications.
Develop high-quality SQL and Python-based data processing solutions.
Implement batch and incremental data-processing patterns.
Develop reusable engineering components and frameworks.
Support complex data migration and transformation activities.
Perform source-to-target mapping and technical data analysis.
Implement pipeline logging, monitoring, exception handling, and recovery mechanisms.

Database & Platform Engineering

Design and optimize relational database schemas and data structures.
Analyze and improve SQL/query performance.
Support indexing, partitioning, storage optimization, and database performance.
Implement appropriate database security and access controls.
Support database monitoring, availability, backup/recovery, and operational requirements in collaboration with infrastructure/platform teams.
Troubleshoot database and pipeline performance issues.
Contribute to cloud data-platform configuration and optimization.

Data Quality

Implement automated validation and data-quality controls.
Develop reconciliation processes between source and target systems.
Identify duplicate, incomplete, inconsistent, and invalid data.
Implement cleansing, standardization, validation, and exception-management logic.
Work with technical and business stakeholders to understand and implement business data rules.

Technical Delivery

Convert technical designs into reliable production solutions.
Participate in technical design and code reviews.
Mentor junior/mid-level engineers.
Support estimation and technical planning.
Troubleshoot complex production and integration issues.
Collaborate with technical leads, application teams, DevOps, QA, BI, and client teams.
Document technical designs, mappings, configurations, operational procedures, and engineering decisions.

Consulting & AI-Led Working

Understand the business purpose behind assigned engineering requirements.
Raise risks, assumptions, dependencies, and potential improvements proactively.
Communicate effectively with client and internal stakeholders.
Use AI-assisted tools to improve coding, testing, analysis, documentation, and troubleshooting productivity.
Independently verify AI-generated code and recommendations before implementation.
Identify opportunities for engineering automation and process improvement.

Required Qualifications

6–9 years of experience in data engineering, database engineering, data integration, or related areas.
Advanced SQL skills.
Strong hands-on Python experience.
Strong ETL/ELT development experience.
Strong relational database knowledge, such as SQL Server, PostgreSQL, Oracle, or equivalent.
Experience with data modeling and database design.
Experience with AWS, Azure, or another major cloud platform.
Experience with modern data technologies such as Databricks, Snowflake, Redshift, Synapse, Glue, ADF, Airflow, dbt, or equivalent.
Experience with performance tuning and optimization.
Experience with data validation and reconciliation.
Strong troubleshooting and analytical skills.
Ability to take ownership of engineering deliverables with limited supervision.

Preferred Qualifications

Experience working with enterprise ERP, CRM, PIM, PLM, MES, or related systems.
Experience with manufacturing, retail, distribution, or consumer-goods data.
Experience with CI/CD and infrastructure/platform engineering practices.
Experience with API and event-driven integrations.
Previous consulting or technology-services experience.

What Success Looks Like

Pipelines and databases perform reliably at enterprise scale.
Engineering solutions are maintainable, reusable, and well documented.
Data issues are detected and addressed systematically.
Junior engineers receive effective technical guidance.
Problems are owned through resolution rather than simply escalated.
Engineering decisions reflect both technical requirements and client business objectives.

Read more

VY SYSTEMS PRIVATE LIMITED

Data Engineer

at VY SYSTEMS PRIVATE LIMITED

1 recruiter

Dharani S

Posted by Dharani S

Bengaluru (Bangalore)

5 - 9 yrs

₹3L - ₹20L / yr

Python

DevOps

PySpark

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines.
Develop data processing solutions using Python.
Write complex and optimized SQL queries, stored procedures, and data transformations.
Build and maintain data ingestion and integration workflows.
Implement data quality, validation, monitoring, and error-handling processes.
Develop and maintain CI/CD pipelines for data engineering applications.
Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
Collaborate with data analysts, data scientists, software engineers, and business teams.
Optimize data pipelines for performance, reliability, and scalability.
Troubleshoot production data issues and ensure timely resolution.
Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

Python
ETL
SQL
CI/CD
DevOps
Git / Version Control
Strong problem-solving and debugging skills

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines.
Develop data processing solutions using Python.
Write complex and optimized SQL queries, stored procedures, and data transformations.
Build and maintain data ingestion and integration workflows.
Implement data quality, validation, monitoring, and error-handling processes.
Develop and maintain CI/CD pipelines for data engineering applications.
Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
Collaborate with data analysts, data scientists, software engineers, and business teams.
Optimize data pipelines for performance, reliability, and scalability.
Troubleshoot production data issues and ensure timely resolution.
Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

Python
ETL
SQL
CI/CD
DevOps
Git / Version Control
Strong problem-solving and debugging skills

Read more

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