Databricks Architect - India
Databricks Architect — Senior / Specialist
The Role
We're seeking a Databricks Architect (Senior/Specialist level) to serve as a senior technical authority for data architecture and analytics engineering on the Databricks Lakehouse platform across Cognify Analytics and our client engagements. In this role, you will design and own Databricks-based data platform architecture, lead complex data engineering initiatives, and ensure our data capabilities are enterprise-grade, governed, and future-ready. You will bridge data engineering, analytics, and AI/ML — combining deep hands-on expertise in Databricks, Python, and SQL with strong architectural judgment and stakeholder communication.
Must-Have Skills (Non-Negotiable)
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Databricks — hands-on architecture and engineering experience on the Databricks Lakehouse Platform (production-grade, at scale)
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Python — strong professional proficiency for data engineering and pipeline development
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SQL — advanced proficiency for data modeling, transformation, and performance tuning
Candidates without demonstrable, hands-on experience in all three of the above will not be considered.
What You'll Do
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Own the architecture and design of Databricks-based data platforms, including lakehouse design (Delta Lake), medallion architecture, and unified analytics layers.
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Serve as the senior technical authority on Databricks platform design, providing guidance on architecture, tooling, modeling, and engineering standards across teams and projects.
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Design and build ingestion, transformation, and orchestration pipelines using Python, SQL, PySpark, and Databricks Workflows.
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Architect data mesh and data product strategies, defining domain ownership, data contracts, and self-service consumption patterns on Databricks.
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Establish and evangelize best practices across the data lifecycle: ingestion, transformation, modeling, quality, observability, governance, and consumption.
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Drive the integration of Databricks with AI/ML capabilities, including feature engineering pipelines, vector data infrastructure for RAG, and MLflow-based model workflows.
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Lead complex data migration, platform modernization, and consolidation initiatives for enterprise clients across industries.
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Evaluate emerging data technologies (Apache Iceberg, Unity Catalog, Delta Live Tables, Mosaic AI) to inform architectural decisions.
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Solve complex, ambiguous, high-impact data architecture problems that span multiple teams, platforms, or organizational boundaries.
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Drive cross-functional alignment between data engineering, analytics, AI/ML, platform engineering, security, and product teams.
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Mentor senior data engineers and analysts, fostering a culture of technical excellence.
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Represent Cognify Analytics in discussions with client stakeholders and leadership on data capabilities, strategy, and technical roadmaps.
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Define and enforce data governance, compliance (GDPR, HIPAA, SOC2), and responsible data management standards across all Databricks environments.
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Drive FinOps maturity for the Databricks platform, including compute optimization, cluster policies, storage lifecycle management, and cost forecasting.
What We're Looking For
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Bachelor's, Master's, or equivalent professional experience in Computer Science, Data Science, Statistics, or a related field.
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6–9 years of professional experience in data engineering, analytics engineering, or data architecture, with demonstrated technical leadership on at least a few large-scale engagements.
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Mandatory, hands-on expertise in Databricks, Python, and SQL in production environments.
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Strong working knowledge of the modern data stack: dbt, Airflow/Dagster, Fivetran/Airbyte, Spark, Kafka, and cloud-native data services.
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Solid grasp of data modeling methodologies (Kimball, Data Vault, Activity Schema, OBT) and the judgment to apply them across contexts.
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Experience with cloud data infrastructure on AWS, Azure, or GCP (any co