Data Architect

About the Role
We are seeking a Technical Solution Architect with 14+ years of experience to own the end-to-end architectural integrity of this program, from component design, through implementation oversight and deployment, to operationalization. The project centers on a data lakehouse platform that ingests data from heterogeneous sources, organizes it through semantic layers, exposes it via REST APIs, and is being enhanced with AI-powered natural language query capabilities and entity resolution.
Responsibilities
Key Responsibilities
End-to-end solution architecture across ingestion, storage, semantic modelling, API exposure, governance and observability.
Component-level design reviews: ingestion pipelines, semantic-layer patterns, API contracts, caching, event streaming, access-control boundaries.
Implementation oversight: to ensure it matches the architecture, design quality, security, performance and cost.
Technical decision-making on build-vs-buy, tooling and pattern choices, with clear written rationale.
Being the architectural point of contact for client stakeholders, engineering managers, tech leads and data science / analytics consumers.
Qualifications
Required Qualifications
14+ years of hands-on engineering experience, with 3+ years in a solution / data architect capacity.
Strong AWS experience: comfortable designing on S3, Lambda, API Gateway, IAM and typical data services.
Deep experience with at least one modern data platform paradigm: data lake, lakehouse, data warehouse, data mesh or data fabric on platforms such as Snowflake, Databricks, Redshift, BigQuery, Synapse, or equivalent.
Solid grounding in semantic / layered data modelling (raw → business → consumption) and in the trade-offs between virtualization, materialization and copy-based pipelines.
Strong Python for data pipelines and backend services; comfortable reviewing and writing production-grade code.
Experience designing and operating REST APIs at scale: versioning, auth, rate limits, pagination, observability, contract discipline.
Working knowledge of API security and governance patterns: JWT/OAuth, tenant isolation, RBAC / ABAC, field-level access control, audit logging.
Hands-on experience with real-time event streaming using Kafka, Kinesis, Pulsar or equivalent: producers/consumers, partitioning, ordering and delivery guarantees, schema evolution, back-pressure.
Required Skills
Production experience with in-memory caching (Redis, Memcached or equivalent) for low-latency reads: cache keys, TTLs, invalidation, hot-key mitigation, and consistency trade-offs.
Working experience with data lineage, cataloguing and governance tooling (e.g. DataHub, OpenMetadata, Collibra, Alation, Unity Catalog, AWS Glue Data Catalog, or equivalent).
LLM / RAG patterns applied to structured data: prompt design, grounding against catalogues and dictionaries, evaluation.
Clear written and verbal communication; able to explain trade-offs to engineers, to engineering managers, and to non-technical stakeholders.
Preferred Skills
Preferred Qualifications
Open table formats in production: Apache Iceberg, Delta Lake or Apache Hudi.
Query / acceleration engines such as Dremio, Trino, Presto, Starburst or Athena.
Entity resolution / record linkage, either hand-rolled or via tools such as Zingg, Senzing or AWS Entity Resolution.
Multi-tenant SaaS data platform experience, especially around purpose-of-use controls and regulated workloads.
Consulting or client-facing delivery experience.
Soft Skills
Strong analytical and problem-solving skills.
High attention to detail.
Ownership mindset and proactive attitude.
Ability to work collaboratively in cross-functional environments.
Additional Information
Location: Noida / Remote / Hybrid
Employment Type: Full-time

Work arrangement
No