Data Architect

Job Summary:

You will define and own Mindsprint's enterprise data architecture: cloud lakehouse platforms, ingestion and streaming pipelines, canonical data models and governance. The platforms you design will feed enterprise analytics and our GenAI and agentic AI workloads.

Job Description:

• Define data strategy, reference architectures, architecture principles and target-state roadmaps aligned to business goals.
• Design enterprise-scale data lakes, lakehouses, modern data warehouses and common data platforms on Azure and Databricks (Microsoft Fabric a plus).
• Architect batch and real-time pipelines (Spark, Kafka, Event Hubs, ADF, Airflow) for structured, semi-structured and unstructured data.
• Lead ETL/ELT modernization and migrations from on-premise Hadoop, Informatica or legacy warehouses to cloud.
• Build reusable, metadata-driven frameworks for ingestion, data quality, lineage and integration testing, and drive their adoption across delivery teams.
• Set standards for canonical data models, API-led and event-driven integration, and data products.
• Own data governance and security: cataloguing, lineage, access control (Unity Catalog, Purview, Ranger/Atlas), privacy and compliance.
• Prepare data foundations for AI: feature stores, document pipelines, vector and graph stores for RAG and knowledge-graph use cases.
• Run architecture reviews, performance tuning, scalability planning and cloud cost optimization.
• Advise clients and executives, lead PoCs, and support RFP/RFI responses and solution presentations.
• Mentor data engineers and architects across global teams.

Profile Description:

Must-have:

• 15+ years in data engineering and architecture, including 5+ years as a lead or principal architect.
• Deep hands-on Azure data stack: Data Factory, Databricks, Synapse, Event Hubs, Cosmos DB, Functions.
• Strong Spark (PySpark/Scala), SQL and Python; Delta Lake / lakehouse design.
• Streaming with Kafka or Spark Structured Streaming.
• Delivered at least two large cloud data platform or migration programs end to end.
• Data modelling (dimensional, Data Vault, canonical) and data governance frameworks.
• Clear communicator with executive stakeholders.

Good to have:

• AWS (Glue, EMR, Redshift) or GCP (BigQuery, Dataflow) experience.
• Microsoft Fabric, dbt, Unity Catalog, Purview.
• Hadoop ecosystem background (Hive, HBase, Cloudera/Hortonworks).
• Graph databases (JanusGraph, Neo4j) and search (Solr, Elasticsearch).
• Domain experience in supply chain, agri/food, manufacturing, energy or retail.
• Certifications: Azure Data Engineer / Solutions Architect, Databricks, AWS or GCP data.

Education: B.E./B.Tech or M.Tech in Computer Science, Electronics or a related field.