Data Architect

2 weeks ago

Bangalore, India Grid Dynamics Full time

Role Overview The Data Architect will design and govern scalable big data and analytics platforms on any leading cloud (AWS/Azure/GCP), working closely with sales and delivery teams to shape solutions for prospective and existing clients. The role combines hands‑on architecture, technical leadership, and presales responsibilities, with a strong focus on SQL, Python, and modern data engineering practices. Key Responsibilities Design and own end‑to‑end data architectures including data lakes, data warehouses, and streaming pipelines using big data technologies (e.g., Spark, Kafka, Hive) on public cloud platforms. Define canonical data models, integration patterns, and governance standards to ensure data quality, security, and compliance across the organization. Lead presales activities: assess client requirements, run discovery workshops, define solution blueprints, size and estimate effort, and contribute to RFP/RFI responses and proposals. Build and review PoCs/accelerators using SQL and Python (e.g., PySpark, notebooks) to demonstrate feasibility, performance, and business value to customers. Collaborate with data engineers, BI/ML teams, and application architects to ensure the designed architecture is implemented as intended and is cost‑efficient, scalable, and reliable. Establish best practices for data security, access control, and lifecycle management in alignment with regulatory and enterprise policies. Monitor and continuously optimize data platforms for performance, reliability, and cost, leveraging cloud‑native services and observability tools. Provide architectural guidance and mentoring to engineering teams; review designs and code for critical data components. Required Skills & Experience 12-16 years of overall experience in data engineering/analytics, with 4+ years as a Data/Big Data Architect. Strong expertise in SQL (analytical queries, performance tuning) and Python for data processing and automation. Hands‑on experience with big data frameworks and tools such as Spark, Kafka, Hadoop ecosystem, distributed file systems, and modern ETL/ELT pipelines. Practical experience on at least one major cloud platform (AWS, Azure, or GCP) with services such as data lakes, warehouse services (Redshift/Snowflake/BigQuery/Synapse), and orchestration tools. Proven presales exposure: client workshops, solution design, RFP/RFI responses, effort estimation, and building PoCs or demos. Strong understanding of data modeling (OLTP, OLAP, dimensional modeling), data governance, security, and compliance. Ability to communicate complex data solutions clearly to both technical and business stakeholders.

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