Data Analytics Architect

Key Responsibilities: Design and develop modern data architectures across cloud, on-prem, or hybrid environments. Define data strategy, integration patterns, and analytics platform roadmap in alignment with business goals. Architect scalable data pipelines, data lakes, data warehouses, and analytics platforms (e.g., Snowflake, Azure Synapse, BigQuery). Lead the design of ETL/ELT processes, real-time data ingestion, and data transformation workflows. Collaborate with enterprise architects, data engineers, and business teams to ensure architecture alignment. Evaluate and select suitable data platforms, tools, and frameworks for analytics, reporting, and AI/ML workloads. Govern data models, metadata management, data quality, lineage, and security standards. Provide technical leadership and best practices for the delivery of data and analytics projects. Conduct architecture reviews, cost-benefit analysis, and scalability assessments. Mentor development teams and contribute to capability building across the data organization. Required Skills and Qualifications: 10+ years of experience in data engineering, BI, analytics, or architecture. 3-5 years as a data architect or solution architect in analytics-heavy environments. Expertise in cloud data platforms (e.g., AWS Redshift, Azure Synapse, Snowflake, GCP BigQuery). Deep understanding of data modeling, data warehousing, and lakehouse architectures. Strong experience in SQL, Python, and data pipeline orchestration tools (e.g., Airflow, dbt, ADF). Knowledge of streaming and real-time processing (e.g., Kafka, Spark Streaming, Kinesis). Familiarity with BI tools (e.g., Power BI, Tableau, Looker) and how they consume data models. Experience with data governance, data security, and regulatory compliance (e.g., GDPR, HIPAA). Strong communication and stakeholder management skills. Familiarity with DevOps and CI/CD practices in data projects. Preferred Qualifications: Cloud certifications (e.g., AWS Certified Data Analytics, Azure Data Engineer, GCP Data Engineer). Experience with AI/ML pipelines and integration with analytics systems. Prior experience in sectors like banking, retail, healthcare, or manufacturing. Exposure to Master Data Management (MDM) and Data Cataloging tools (e.g., Collibra, Alation).