Bluecopa Full-time Hyderabad

Senior Data Engineer

Location: Hyderabad (Work from Office)

Type: Full-time | 5 Days a Week

Experience : 5-8 Years

About Bluecopa

Bluecopa is an AI-native finance operations platform that automates Record-to-Report (R2R), Order-to-Cash (O2C), and Procure-to-Pay (P2P) on a single unified data spine — so finance teams close faster, reconcile at scale, and run on real-time, trustworthy numbers. We exist to bring finance into its next age of tooling, and eventually make it autonomous.

The Role

We're looking for a Senior Data Engineer with 5–8 years of hands-on experience in data integration, pipelines, and transformations.

You'll work with Python, dbt, and modern data warehouses to connect multiple data sources — SaaS platforms, databases, and file-based or object storage systems like GCS, S3, Azure Blob, SFTP, Google Drive, and OneDrive — and ensure the data foundation is reliable, consistent, and analytics-ready.

This is not a support role. You'll own integrations end-to-end — design, build, maintain, and improve. You'll work closely with Analytics, Platform, and Product teams and will be expected to bring both technical depth and good judgment to decisions about data quality, scalability, and cost.

What You'll Do

Data Pipeline Engineering: Design, build, and maintain data integration pipelines across SaaS platforms, databases, and object storage systems (GCS, S3, Azure Blob, SFTP, Google Drive, OneDrive).
Workflow Management: Build and manage ELT/ETL workflows using Python and dbt.
Data Warehousing: Model, transform, and organize data in modern warehouses — BigQuery, Snowflake, or Databricks.
Tool Integration: Integrate and sync data using iPaaS and data movement tools such as Airbyte, Singer, CData, or Dell Boomi.
Cross-functional Collaboration: Collaborate with Analytics, Platform, and Product teams to define data models, transformations, and data contracts.
Performance Optimization: Optimize workflows for scalability, performance, and cost-efficiency.
Data Governance & Quality: Implement data quality checks, validation, and observability mechanisms.
Engineering Best Practices: Follow best practices for version control, CI/CD, and orchestration of data jobs.

What We're Looking For

Core (All Mandatory)

Experience: 5–6 years of experience in data engineering or data integration roles.
Programming: Strong hands-on Python — this is non-negotiable.
Data Modelling: Proven experience with dbt for data modelling, transformation, and deployment.
Data Warehouses: Hands-on with at least one modern data warehouse: BigQuery, Snowflake, or Databricks.
Database Skills: Excellent SQL skills and experience with database performance tuning.

Integration & Tooling

iPaaS Platforms: Experience with one or more iPaaS / data integration tools: Airbyte, Singer, CData, or Dell Boomi.
Data Replication: Experience with Change Data Capture (CDC) — Debezium or similar tools for real-time data streaming and replication.
Cloud Infrastructure: Familiarity with cloud platforms: GCP, AWS, or Azure.

Ways of Working

Version Control: Experience with Git and collaborative development workflows.
Autonomy: Comfortable working across cross-functional teams and owning deliverables independently.

Good to Have

Experience with API integrations and RESTful data ingestion.
Knowledge of data orchestration tools like Airflow or Prefect.
Exposure to data governance, cataloguing, or metadata management.
Prior experience in SaaS or high-growth startup environments.

Mindset

High Ownership: Technically sharp. You care about data quality and reliability — not just shipping pipelines. You're comfortable with ambiguity, take initiative when things are unclear, and communicate proactively when something is at risk.
Proactivity: You don't wait to be told what to do. You find the problem, scope it, and fix it.

Your application

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Additional questions

What is your experience using Python to build custom data integration pipelines, specifically when handling data ingestion from sources like APIs, SFTP, or cloud object storage (GCS/S3)? *

We rely heavily on dbt for data modeling and transformation. Can you briefly describe how you have structured dbt models and managed macro or snapshot implementations in production alongside a modern data warehouse (BigQuery, Snowflake, or Databricks)? *

What is your practical experience working with data movement or iPaaS tools such as Airbyte, Singer, CData, or Dell Boomi? Please mention if you have handled real-time data replication using Change Data Capture (CDC) tools like Debezium. *

Given that you will optimize workflows for both scalability and cost-efficiency, what is a specific strategy or database performance tuning action you have taken recently to reduce warehouse compute costs or optimize complex SQL queries? *

We look for engineers who don't wait to be told what to do, but instead actively find, scope, and fix data reliability problems. Can you share a quick example of a time you proactively identified a flaw or pipeline failure in a data ecosystem and took complete ownership to resolve it? *

Please note that once you apply, we will be sending a game-based assessment test. The breakdown of the test is 70% aptitude and 30% skill-based, and it will require a dedicated 2-hour time slot. Please let us know your preferred availability to take this assessment so we can schedule it according to your preference. (Below is the example) *

Example: 02/06/2026 11 AM to 1 PM

Referred by an employee or agency? (Please specify name, if any)

Your information is kept confidential.