Data Architect

6 days ago

Bangalore, India Dautom Full time

Data Architecture Design & Strategy: ○ Lead engagements with the internal stakeholders to understand their data needs and design solutions ensuring data quality and governance. ○ Design the right pipeline architecture to handle data and support various use cases, including analytical reporting and machine learning. ○ Design and evolve scalable and modular enterprise data architecture (including data lakes, data warehouses, real-time streaming, and operational data stores) to support current and future data needs. ○ Lead end-to-end architecture for data platforms including ingestion, storage, modeling, access, and integration. ○ Define and enforce data modeling standards and practices (e.g., star/snowflake schema, 3NF, Data Vault), ensuring consistency, maintainability, and performance. ○ Partner with cross-functional teams (Engineering, Analytics, Data science, Product, Sales, Marketing etc) to translate business requirements into reliable and robust data architectures. ○ 10+ years of experience designing and implementing enterprise-scale data solutions, including data warehouses, data lakes, and real-time systems. ● Hands-on of hands-on experience building and orchestrating ETL/ELT data pipelines using tools such as dbt, Apache Airflow, Glue, Airbyte, Matillion, or Stitch. ● 4+ years of experience in data modeling, schema design, performance optimization, and database architecture for both OLTP and OLAP systems. ● Strong proficiency in SQL, with demonstrated ability to write, debug, and optimize complex queries for performance and scalability. ● Solid programming skills in Python (preferred) or Java, with experience in building data transformation and integration logic. ● Deep understanding of AWS data services (such as S3, RDS, DynamoDB, Glue, Lambda, EMR, ECS) and cloud data warehouse platforms like Snowflake. ● Expertise in performance tuning, pipeline debugging, and optimizing data workflows in large-scale environments. ● Experience deploying and maintaining enterprise data platforms that support analytics, BI, and machine learning use cases at scale. ● Familiarity with distributed data processing frameworks such as Apache Spark, Hadoop, and Apache Kafka for real-time and big data processing. ● Strong foundation in software engineering best practices, including version control, CI/CD, modular design, and testing. ● Proven ability to lead and deliver complex data architecture projects in cloud-native environments, with excellent stakeholder and cross-functional collaboration skills. ● Good understanding of software engineering principles and standards.

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