Immediate Job Opening For Data Warehouse Architect - Nuziveedu Seeds

Exp: 8+Yrs

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Job Title: Data Warehouse Architect

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About the Role

Data Warehouse Architect

Key Responsibilities

Data Architecture & Engineering

• Design and implement scalable

  data lake and data warehouse architectures

  using cloud or hybrid platforms.
• Develop

  enterprise data models, data marts, and semantic layers

  to support department-specific requirements.
• Define data ingestion frameworks (batch and real-time) using ETL/ELT tools and pipelines.
• Architect data structures to support analytics, reporting, machine learning, and AI-driven workflows.

Data Governance & Optimization

• Ensure

  data quality, integrity, and consistency

  across all enterprise systems.
• Implement

  metadata management, data cataloging, lineage tracking, and access control

  .
• Optimize data storage, query performance, and cost across the data platform.

Cross-Functional Enablement

• Work with business stakeholders to convert requirements into

  logical and physical data models

  .
• Build

  subject-specific data marts

  (finance, HR, production, marketing, etc.) for performance analytics.
• Partner with data science teams to

  enable feature stores, ML-ready datasets, and AI model deployment pipelines

  .

Innovation & Automation

• Lead modernization initiatives using

  cloud-native technologies, lakehouse architectures, and data fabric models

  .
• Integrate advanced analytics and predictive intelligence capabilities into the data platform.
• Evaluate and implement best practices for data security and compliance.

Required Skills & Qualifications

• Bachelors or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

• 7+ years of experience

  in data engineering, data architecture, or enterprise analytics roles.
• Strong expertise in:

• Data lake & warehouse technologies:

  Snowflake, Redshift, Azure Synapse, Google BigQuery, Databricks, Hadoop, etc.

• ETL/ELT Tools:

  Informatica, Talend, dbt, Azure Data Factory, AWS Glue, etc.

• Cloud Platforms:

  AWS, Azure, GCP.

• Programming:

  SQL, Python, Scala.

• Data Modeling:

  Kimball/Inmon methodologies, dimensional modeling, star/snowflake schemas.

• Data Storage:

  Delta Lake, Parquet, ORC, Hive Metastore.
• Knowledge of

  data science concepts and ML workflows

  , including support for model training and feature engineering.
• Experience creating

  data marts for department-specific analytics

  .

Preferred / Good to Have

• Hands-on experience with

  lakehouse architectures

  (e.g., Databricks, Snowflake).
• Knowledge of Airflow, Kafka, Spark Streaming, or real-time data processing.
• Exposure to BI tools: Power BI, Tableau, Qlik, or Looker.
• Experience with

  governance, MDM (Master Data Management), and data security frameworks

  .

Key Attributes

• Strategic thinker with a

  data-first, analytics-driven mindset

  .
• Strong stakeholder management and communication skills.
• Ability to translate complex data concepts into simple business language.
• Innovative and future-focused, with interest in evolving data & AI technologies.

Career Growth Opportunities

• Pathways to become

  Chief Data Architect

  ,

  Head of Data Platform

  , or

  AI & Analytics Leader

  .
• Ownership of enterprise data strategy and involvement in next-gen data initiatives.

Why Join Us

• Opportunity to build data foundations for an AI-powered enterprise.
• Work across departments and drive business impact at strategic levels.
• Collaborate with cross-functional teams and innovate on cloud and ML-ready architectures.