Principal Implementation Data Architect

Function: Professional Services / Customer Solutions
Reports to: Head of Professional Services (Delivery)
Location: Hyderabad / Bengaluru (Work from Office)
Experience: 10–12 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 Principal Implementation Data Architect is the senior technical authority for customer data onboarding at Bluecopa. You own the architecture and the standards: how we read a customer’s data landscape, which connectors we deploy, how we prove the data can be trusted, how we size ingestion responsibly, how we keep everything auditable, and how we complete the handshake back to a customer’s ERP or downstream systems. You lead the most complex and strategic engagements yourself, and you set the patterns that let every other engagement go faster and safer. You are equal parts hands-on architect, customer-facing advisor, and practice builder.

Key Responsibilities

• End-to-End Data Architecture: Own the end-to-end data architecture for customer implementations: assess each customer’s data landscape, source systems, and data maturity, and define the right ingestion and integration approach.
• Connector Strategy: Define connector strategy — determine which Bluecopa connectors apply for a given customer, identify gaps, and specify the approach where a standard connector does not exist.
• Data Validation Standards: Establish and own data validation standards across completeness, correctness, and veracity, so downstream reconciliations and close processes run on data the customer can trust.
• Reference Designs: Architect ingestion pipelines that feed downstream solutions, and define the reusable patterns, templates, and reference designs the wider Professional Services team builds against.
• Sizing & Capacity Planning: Own ingestion sizing and capacity planning — produce defensible approximations that account for data volume, cost, and velocity, and set the guardrails the team uses to size new engagements.
• Audit & Enforced Lineage: Define and enforce audit best practices — data lineage, controls, reconciliation checkpoints, and traceability — embedded into every pipeline by design.
• Reverse-ETL Architecture: Architect reverse-ETL pipelines, APIs, and data formats, and own the integration design that completes the handshake with the target ERP or external system.
• Technical Point of Contact: Act as the senior technical point of contact for customer data and engineering stakeholders during scoping and delivery; support pre-sales and solution scoping for complex or strategic accounts.
• Practice Scale & Productization: Productize what works — turn repeated bespoke work into reusable connectors, sizing models, validation frameworks, and integration patterns so the practice scales.
• Team Mentorship: Mentor Senior and other Implementation Data Architects; review designs, raise the technical bar, and grow the team’s capability.

Required Qualifications

• Experience: 10–12 years in data engineering, data integration, or solutions/implementation architecture, with a substantial portion in customer-facing or delivery roles.
• Scale Delivery: Proven track record architecting and delivering production data pipelines (ETL/ELT) at scale, ideally for transaction-intensive or financial data.
• Technical Depth: Deep, hands-on expertise with SQL and a programming language such as Python, and with modern data pipeline and orchestration tooling (e.g., Airflow, dbt, Spark, or equivalents).
• Integration Experience: Strong integration experience across REST APIs, webhooks, file/SFTP-based feeds, and message-based integration, including reverse-ETL patterns back into operational systems.
• ERP System Ingestion: Direct experience integrating with ERP and finance systems (e.g., NetSuite, SAP, Oracle, Microsoft Dynamics, QuickBooks, Zoho, Tally) and with payment, banking, or transactional data sources.
• Infrastructure Costing: Demonstrated ability to size and cost data ingestion — translating volume and velocity into infrastructure, cost, and performance estimates.
• Governance & Compliance: Strong grounding in data quality and validation methodology, and in audit, lineage, and governance practices; familiarity with compliance contexts (e.g., SOC 2, ISO 27001, GDPR) is expected.
• Stakeholder Communication: Excellent communication skills — able to be the trusted technical voice in front of customer finance and engineering leaders.

Preferred

• Experience in a SaaS professional services, forward-deployed, or solutions architecture function.
• Background in financial operations, reconciliations, or the close process, or in FP&A / finance data domains.
• Experience building reusable connector frameworks or integration platforms.
• Cloud data platform depth (Snowflake, BigQuery, Redshift, or Databricks).

What Success Looks Like

• New customers reach a clean, validated, production data flow faster and with fewer surprises.
• Sizing estimates hold up against real production volume and cost.
• Audit and lineage are built in, not bolted on, across every engagement.
• The team has reusable patterns instead of reinventing each onboarding from scratch.