Data Engineer

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.

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 and Validation: Validate ingested data for completeness, correctness, and veracity, and implement the data-quality checks that protect downstream reconciliation and close processes.
• Sizing and Estimation: Apply ingestion sizing guidance to estimate volume, cost, and velocity for your engagements, and surface where reality diverges from the estimate.
• Audit and 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.

Requirements:

• 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.