Solutions Architect - Data Engineering
New
P
phDataData and AI consultancy
Location: India - BangaloreFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience in Solutions Architecture, Data Engineering, or related consulting roles.
- Required Skills
- PythonSQLCloud ComputingSnowflakeData engineeringCI/CDDatabricks
Requirements
- 8+ years of experience in Solutions Architecture, Data Engineering, or related consulting roles.
- Proven experience designing and delivering enterprise Data & AI solutions into production within cloud environments.
- Demonstrated experience delivering with AI, leveraging modern AI technologies throughout the software delivery lifecycle.
- Strong consulting experience leading technical discovery, solution architecture, and client-facing engagements.
- Hands-on experience with cloud and data platforms such as Snowflake, Databricks, Google BigQuery, AWS, Microsoft Azure, or GCP.
- Hands-on experience with data engineering technologies including Python, SQL, Java/Scala, Spark, Kafka, Airflow, dbt, Fivetran, or Matillion.
- Experience with modern engineering practices including CI/CD, Infrastructure as Code, observability, and monitoring.
- Experience incorporating AI technologies like Anthropic Claude, OpenAI, GitHub Copilot, or LangChain into engineering workflows.
Responsibilities
- Lead discovery sessions to uncover business goals, technical requirements, and solution constraints, while translating complex client challenges into scalable, production-ready Data & AI architectures.
- Serve as the primary technical advisor throughout client engagements.
- Provide clear recommendations on architecture, tooling, implementation approaches, and technical trade-offs, while developing architecture diagrams, design documentation, implementation plans, and technical standards.
- Lead and own the successful delivery of enterprise Data & AI solutions from architecture through production deployment and guide engineering teams through implementation while proactively identifying technical risks and removing delivery blockers.
- Leverage AI throughout the software delivery lifecycle to improve solution design, engineering productivity, documentation, testing, troubleshooting, and client outcomes while guiding clients on practical AI adoption.
- Mentor engineers through architectural guidance, technical coaching, design reviews, and contribute to hiring, onboarding, and technical enablement.
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