Mindsprint
Data Architect
- Compensation
- Not disclosed
- Location
- Bangalore, Karnataka, India
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Architecture
- Posted
Skills
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.
Nice 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.
Full description
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.