• Architect and maintain cutting-edge data systems that power analytics, AI, and operational decision-making. • Take ownership of end-to-end data lifecycles, designing pipelines, models, and architectures that support real-time insights and machine learning at scale. • Build modern, cloud-native data platforms (AWS, Snowflake, Databricks) supporting batch and streaming use cases. • Automate ETL/ELT workflows, optimize data models, and enable self-serve analytics and AI. • Manage ingestion, storage, processing, and delivery of structured and unstructured data. • Continuously tune infrastructure for high concurrency, low latency, and cost efficiency. • Ingest telemetry, API, and application data in real time to power dashboards and AI-driven tools. • Provision datasets for ML/AI workloads, integrating with SageMaker, Snowflake ML, and MLOps best practices. • Ensure robust data governance, compliance (GDPR, SOC 2), and enterprise-grade security. • Work closely with Product, Engineering, DevOps, and Analytics teams to align data solutions with business goals.

• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and design data models and schemas that facilitate data analysis and reporting • Design, develop, and maintain scalable and efficient data pipelines and ETL processes to ingest, process, and transform large volumes of data from various sources into usable formats • Build and optimize data storage and processing systems, including data warehouses, data lakes, and big data platforms, using AWS services such as Amazon Redshift, AWS Glue, AWS EMR, AWS S3, and AWS Lambda, to enable efficient data retrieval and analysis • Implement and manage real-time data streaming architectures using AWS services like Amazon Kinesis or Apache Kafka to enable real-time data processing and analytics • Perform data profiling, data cleansing, and data transformation tasks to prepare data for analysis and reporting • Implement data security and privacy measures to protect sensitive and confidential data using AWS security services and features • Design and implement data architectures following Data Mesh principles within the AWS environment, including domain-oriented data ownership, self-serve data infrastructure, and federated data governance • Provide technical guidance and mentorship to junior data engineers, reviewing their work and ensuring adherence to best practices and standards

• Collaborate with the Product Owner and team leads to define and design efficient pipelines and data schemas • Build and maintain infrastructure using Terraform for cloud platforms • Design and implement large-scale cloud data infrastructure, self-service tooling, and microservices • Work with large datasets to optimize performance and ensure seamless data integration • Develop and maintain squad-specific data architectures and pipelines following ETL and Data Lake principles • Discover, analyze, and organize disparate data sources into clean, understandable schemas

• Design and evolve scalable, cloud-native data architectures for advanced analytics and AI • Develop and maintain real-time and batch data processing platforms • Define and implement data modeling standards for structured/unstructured data • Integrate innovative technologies (vector databases, LLMs, real-time streaming) • Ensure data quality, lineage, and governance • Collaborate with engineering/product teams to translate business needs into solutions • Optimize platform scalability, cost, and performance in cloud environments • Establish architectural standards for data-driven decision-making