Solutions Architect – Data Engineering & Management

Roles and responsibilities :

Design end-to-end data solutions, including data lakes, lakehouses, real-time streaming pipelines, ETL / ELT workflows, and data integration architectures.

Ensure modularity, scalability, and interoperability for enterprise-wide data ecosystems across hybrid and multi-cloud environments.

Build and optimize DataOps frameworks to improve pipeline automation, data quality, and observability.

Collaborate with engineering teams to reduce data latency, optimize compute / storage costs, and ensure high availability and reliability.

Implement data governance frameworks, including data lineage, cataloging, access controls, and quality metrics.

Ensure compliance with GDPR, CCPA, ISO 27001, and other relevant standards for privacy, security, and ethical data use.

Partner with Sales, Business Development, and Delivery teams to craft data engineering solutions aligned with client needs.

Support RFP responses, pricing models, and deal structuring with robust cost modeling and architecture justification.

Lead client workshops, PoCs, and solution demonstrations to showcase TP.ai Data Services capabilities.

Work with Finance and Product teams to develop pricing strategies based on cost-to-serve, consumption, and value-driven models for data platforms and managed services.

Benchmark against industry pricing trends to ensure competitiveness.

Stay at the forefront of emerging data technologies (orchestration frameworks like Airflow, Dagster, Prefect, data mesh architectures, AI-ready data pipelines).

Provide internal training and external advisory on data engineering best practices, modernization strategies, and ROI-driven data transformation.

Engage with AI / ML, Security, Operations, and Trust & Safety teams to ensure integrated, compliant, and future-proof data environments.

Preferred Qualifications :

Hands-on experience with orchestration and workflow management tools (Airflow, Dagster, Prefect) and data mesh or fabric architectures.

Exposure to AI / ML data enablement (data curation, annotation pipelines, ML-ready datasets).

Familiarity with managed data services pricing, cost modeling, and optimization frameworks.

Industry expertise across Technology, BFSI, Retail, Gaming, or Healthcare verticals.

Thought leadership demonstrated via publications, speaking engagements, or patents in Data Engineering or Data Management.