Data Architect

Role Objective The Data Architect is responsible for defining, designing, and implementing enterprise data architecture strategies and solutions that enable data-driven decision-making and digital transformation. The role ensures that the organization’s data assets are structured, governed, and managed effectively to maximize business value, operational efficiency, and compliance. The incumbent provides strategic and technical leadership across big data, data warehousing, analytics, and integration domains, ensuring architectural consistency, scalability, and alignment with enterprise goals.

Key Responsibilities

Enterprise Data Architecture & Strategy

Develop and maintain an enterprise-wide data architecture blueprint aligned with business strategy, digital transformation goals, and IT governance.

Design scalable, secure, and high-performance data platforms that support transactional, analytical, and AI workloads.

Establish data modeling standards (conceptual, logical, and physical), ensuring consistency and reusability across domains.

Define and manage data integration, interoperability, and API-driven architecture patterns.

Translate business requirements into technology solutions and data flows that ensure traceability and compliance.

Partner with enterprise architects to align data architecture with application, infrastructure, and security architectures.

Drive adoption of data mesh, data lakehouse, and cloud-native architectures where appropriate.

Data Management and Governance

Define and implement data standards, taxonomies, and ontologies supporting enterprise data governance.

Collaborate with data stewards, data owners, and governance teams to ensure quality, integrity, and accessibility of enterprise data.

Establish reference architectures and guidelines for data lineage, metadata management, and master / reference data management (MDM / RDM).

Ensure compliance with data privacy, protection, and retention regulations (ISO / IEC 38505-1, PDPPL, GDPR).

Lead periodic architecture reviews and audits to evaluate design adherence, performance, and scalability.

Solution Design and Delivery

Design and deliver data warehouse, big data, and real-time analytics solutions using modern technologies (Spark, Kafka, Snowflake, Databricks, SAP HANA, etc.).

Architect data ingestion and processing pipelines for structured, semi-structured, and unstructured data using ELT / ETL frameworks (e.g., SAP DS, BODS, Informatica, or AWS Glue).

Optimize data flows and database performance to support high-volume workloads and analytics.

Ensure end-to-end data security, accessibility, and disaster recovery.

Support the integration of enterprise data with visualization and BI platforms such as Power BI, Tableau, and Qlik.

Coordinate with cross-functional teams to deliver data-driven solutions that meet business timelines and KPIs.

Stakeholder Engagement and Collaboration

Engage with business leaders and technical teams to understand enterprise goals and translate them into architectural blueprints.

Lead architecture workshops and collaborate with vendors, partners, and external consultants to evaluate and adopt new technologies.

Provide technical guidance and mentorship to data engineers, analysts, and developers.

Support IT leadership in building a culture of data literacy and governance maturity across the organization.

Innovation and Continuous Improvement

Stay current on advancements in data architecture, AI / ML integration, and cloud technologies (AWS, Azure, GCP).

Champion automation, DevOps, and DataOps practices to enhance delivery efficiency and system resilience.

Continuously assess data architecture performance, security, and compliance — implementing refinements as needed.

Drive continuous improvement through adoption of modern practices such as data fabric and event-driven architectures.

Qualifications

Experience : 10+ years of progressive experience in Data Architecture, Big Data, Data Warehousing, or Analytics.

Education : Bachelor’s degree in Computer Science, Information Systems, or a related technical discipline. A Master’s degree is preferred.

Certifications : TOGAF, AWS Certified Data Analytics, DAMA CDMP, Azure Data Engineer, or SAP HANA Certification preferred.

Technical Expertise

Experience with databricks ecosystem (Hive, Spark, HBase, Sqoop, Kafka, Oozie), SAP HANA data modeling, and ETL / ELT tools (BODS, DS, Informatica).

Programming & Scripting

Proficiency in SQL, Python, Scala, R, Shell scripting, and Java.

Tools & Platforms

Deep familiarity with distributed systems, data lakes, data warehouses, and version control systems (Git, SVN).

Knowledge & Skills

Mastery of data architecture principles, modeling methodologies, and integration frameworks.

Strong understanding of cloud data ecosystems, data lakehouse architectures, and hybrid data environments.

Deep expertise in big data technologies and distributed processing frameworks.

Strong analytical, problem-solving, and system design capabilities.

Excellent communication and stakeholder management skills.

Leadership ability to guide data initiatives and manage cross-functional collaboration.

Working knowledge of AI / ML data pipelines, data security frameworks, and governance standards.

Performance Indicators

Accuracy, accessibility, and availability of enterprise data.

Efficiency and scalability of data architecture solutions.

Reduction of data silos and integration complexity.

Compliance with governance and security standards.

Stakeholder satisfaction and project delivery within defined scope and timelines.

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