Data Architect

Responsibilities

• Lead the architecture, design, and implementation of cloud-native data platforms — including ingestion, storage, processing, metadata management, governance, and analytics enablement.
• Define and own data architecture standards, including patterns for data lakes, lakehouse, data warehousing, data marts, and scalable analytics solutions on AWS.
• Collaborate with product management, business stakeholders, and cross-functional teams to translate business requirements into data products and analytics solutions.
• Drive domain-driven data modeling, including canonical data models, dimensional models, and entity relationships aligned to business domains.
• Design and implement scalable data pipelines (batch and streaming) using AWS and modern big-data frameworks (e.g., Spark, EMR, Glue, Athena, Step Functions).
• Architect data solutions leveraging S3-based Data Lake/Lakehouse patterns using open formats like Parquet / Iceberg / Delta (where applicable).
• Build and govern enterprise analytics ecosystems, including semantic layer design, KPI definitions, and reusable curated datasets for BI and downstream consumers.
• Design and optimize data warehouses and analytical query platforms using Amazon Redshift (RA3/Serverless), Athena, and federated query patterns.
• Ensure robust data quality, lineage, observability, and monitoring, integrating with tools/services such as CloudWatch, Dynatrace, data pipeline metrics, and automated validation checks.
• Implement security best practices in data architecture including IAM, encryption (KMS), row/column-level security, PII handling, data masking, and regulatory compliance controls.
• Familiar with version-controlled pipelines, CI/CD automation, environment promotion, and infrastructure-as-code (Terraform/CloudFormation/CDK).
• Architect event-driven and near-real-time analytics solutions using services such as Kafka/MSK, Kinesis, SQS/SNS, and streaming ingestion frameworks.
• Conduct proof-of-concept initiatives to evaluate emerging AWS services and tools that improve performance, cost, and developer productivity.
• Mentor engineering and analytics teams on architecture best practices, query performance optimization, modeling standards, and scalability patterns.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Mathematics, or related field; Master’s preferred.
• 8+ years of progressive experience in data engineering / analytics engineering / platform engineering, with 3+ years in a Data Architect / AWS Data Architecture leadership role.
• Proven experience designing and implementing AWS-native analytics platforms, data lakes/lakehouse, and enterprise-scale BI/analytics architectures.
• Strong hands-on expertise with AWS data services including S3, Glue, Athena, Redshift, EMR, Lake Formation, RDS/Aurora, and orchestration tools like Step Functions / Airflow / MWAA.
• Experience with big data processing using Spark / PySpark, and modern data engineering approaches (partitioning, compaction, incremental processing, CDC patterns).
• Deep proficiency in SQL, performance tuning, and scalable design for analytical workloads.
• Strong understanding of data modeling , metadata/catalog strategies, and data governance concepts.
• Strong understanding of multi-tenant architectures and secure data isolation strategies for analytics workloads.
• Strong communication skills, ability to influence stakeholders, and ability to drive architecture across multiple teams.