Senior Implementation Data Engineer

Function: Professional Services / Customer Solutions
Reports to: Principal Implementation Data Architect / Head of Professional Services Location: Hyderabad / Bengaluru (Work from Office)
Experience: 5–7 years

About the Team

Professional Services is where a new Bluecopa customer’s data reality meets the platform. Bluecopa is an AI-native finance operations platform that centralizes transaction data from ERPs, payment gateways, banks and other systems to automate reconciliations and accelerate the financial close. For a platform whose value rests on accuracy, auditability and real-time sync, the way customer data is brought in, validated, and sent back out is the customer experience. This role owns that data backbone across customer engagements — from the first look at a customer’s landscape through to a clean, trusted, two-way data flow in production.

Role Summary

The Senior Implementation Data Engineer is a hands-on builder who delivers customer data onboarding under the architecture and standards set by the Principal Implementation Data Architect. You take ownership of defined-scope engagements end to end — mapping the customer’s sources, deploying the right connectors, building and validating ingestion pipelines, sizing them sensibly, and completing the reverse-ETL handshake back to the customer’s systems. You work directly with customers, you write the pipelines, and you contribute the patterns you discover back to the practice.

Key Responsibilities

• Data Assessment: Assess a customer’s data landscape and source systems for assigned engagements and translate that into a concrete ingestion plan.
• Connector Configuration: Select and configure the applicable Bluecopa connectors for each customer’s sources; flag gaps where a standard connector does not fit.
• Pipeline Construction: Build and operate ingestion pipelines that feed downstream solutions, following the team’s reference designs and standards.
• Data Quality & Validation: Validate ingested data for completeness, correctness, and veracity, and implement the data-quality checks that protect downstream reconciliation and close processes.
• Sizing & Estimation: Apply ingestion sizing guidance to estimate volume, cost, and velocity for your engagements, and surface where reality diverges from the estimate.
• Audit & Lineage: Implement audit best practices — lineage, controls, and reconciliation checkpoints — as defined by the practice, in every pipeline you deliver.
• Reverse-ETL Handshake: Build reverse-ETL pipelines, APIs, and data formats to complete the handshake with the target ERP or external system.
• Client Engagement: Work directly with customer data and technical contacts during delivery; communicate status, blockers, and data findings clearly.
• Practice Contribution: Contribute improvements, reusable components, and lessons learned back to the team’s patterns and frameworks.

Required Qualifications

• Experience: 5–7 years in data engineering, data integration, or implementation/solutions engineering.
• Core Languages: Strong hands-on SQL and a programming language such as Python, with solid experience building production ETL/ELT pipelines.
• Pipeline Tooling: Working experience with data pipeline and orchestration tooling (e.g., Airflow, dbt, Spark, or equivalents).
• Integration Patterns: Practical integration experience across REST APIs, webhooks, and file/SFTP-based feeds; exposure to reverse-ETL patterns.
• ERP Domain Exposure: Experience integrating with at least one major ERP or finance/transactional system (e.g., NetSuite, SAP, Oracle, Microsoft Dynamics, QuickBooks, Zoho, Tally) or with payment/banking data.
• Data Governance: Solid understanding of data quality and validation techniques, and awareness of audit, lineage, and governance practices.
• Communication: Good customer-facing communication skills and comfort owning a defined-scope engagement to completion.

Preferred

• Experience in a SaaS professional services, implementation, or solutions delivery team.
• Exposure to financial operations, reconciliations, or finance data.
• Familiarity with a cloud data platform (Snowflake, BigQuery, Redshift, or Databricks).
• Awareness of compliance frameworks such as SOC 2, ISO 27001, or GDPR.

What Success Looks Like

• Assigned customers go live with validated, well-sized, auditable data flows.
• Pipelines are built to the team’s standards and need little rework.
• Reverse-ETL handshakes with customer ERPs land cleanly the first time.
• You grow toward owning architecture decisions, not just executing them.