Solutions Architect – Data Engineering

2 weeks ago

Bengaluru, Karnataka, India TP Full time ₹ 12,00,000 - ₹ 24,00,000 per year

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 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.

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