Data Architect (12+years)

Key Responsibilities Define and execute the enterprise data architecture strategy in alignment with business goals and future growth needs.

Architect, design, and implement Data Lakes, Data Warehouses, and Data Lakehouses on cloud platforms.

Lead modernization of data platforms using technologies such as Snowflake, Databricks, Azure Data Factory (ADF) , cloud storage, and related ecosystem services.

Design and implement robust batch and streaming data pipelines using frameworks such as Apache Spark , ensuring scalability, reliability, performance, and data integrity.

Develop frameworks for data ingestion, transformation, orchestration, and automation (ETL / ELT), including real-time streaming ingestion pipelines.

Establish and enforce data governance, metadata management, data cataloging, data lineage and quality standards across the data ecosystem.

Collaborate with business stakeholders, analytics / BI teams, data engineers, and data scientists to understand requirements and deliver data solutions aligned with enterprise needs.

Provide technical leadership, mentor data engineering teams, drive best practices, and ensure maintainability of the data platform.

Keep abreast of emerging data technologies and industry trends; recommend and adopt improvements to continuously evolve the data platform.

Required Skills & Experience 12–15 years of overall IT experience, with at least 8+ years in a Data Architect / senior data-platform architect role.

Proven experience architecting and implementing Data Lakes, Data Warehouses, and Data Lakehouses at enterprise scale.

Strong hands-on expertise with Snowflake (preferred), Databricks, Azure Data Factory, cloud-native storage and services

Deep proficiency in Apache Spark (batch and streaming), data ingestion frameworks, ETL / ELT workflows, and orchestration.

Programming skills in Python for data engineering, automation, custom transformations, and scripting.

Strong knowledge of data modeling, schema design (logical and physical), relational and / or non-relational data stores, query optimization, and performance tuning.

Experience with real-time streaming and event-driven data frameworks (e.g., message queues, event hubs, streaming ingestion).

Solid understanding of cloud-native data ecosystem (preferably Azure, but AWS or GCP experience also relevant).

Experience establishing and enforcing data governance, data cataloging, metadata management, security, and compliance best practices.

Excellent communication, stakeholder management, cross-functional collaboration, and leadership skills.

Education & Background Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.

Proven track record of designing and delivering large-scale data architecture solutions in enterprise environments.