AWS -Senior Data Architect - R01543971

Senior Data Specialist

Primary Skills

Athena, SQS, CloudWatch, Macie, Spark - Scala, Kinesis, CloudFormation, EMR, Open Search, DynamoDB, Amazon API Gateway, SCT, Redshift, DMS, Oozie

Secondary Skills

Python

Job requirements

About Brillio:

Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.

Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.

Work Location: Gurgaon/Hyderabad/Pune/Bangalore/Chennai

Job Title: AWS Data Architect/Senior Lead Data Engineer

Job Type: [Full-time}

Job Summary:

We are looking for an experienced and dynamic AWS Data Architect to lead the design and implementation of data solutions in the cloud. This role will focus on leveraging AWS technologies to create scalable, reliable, and optimized data architectures that drive business insights and data-driven decision-making. As an AWS Data Architect, you will play a pivotal role in shaping the data strategy, implementing best practices, and ensuring the seamless integration of AWS-based data platforms, with a focus on services like Amazon Redshift, Aurora, and other AWS data services.

Key Responsibilities:

Data Architecture Design:

Lead the design and implementation of cloud-native data architectures on AWS, using services like Amazon Redshift, Amazon Aurora, Amazon S3, AWS Glue, and Amazon Athena.

Define data management strategies and ensure data models, structures, and systems are aligned with business goals.

Architect end-to-end data pipelines for ingesting, processing, storing, and analyzing large datasets using AWS services.

Cloud Data Infrastructure Management:

Design and implement highly available, scalable, and secure data infrastructure on AWS.

Lead the migration of on-premise data solutions to AWS cloud platforms, ensuring smooth transitions and minimizing operational impact.

Oversee performance tuning and optimization of data storage and query processing for Amazon Redshift and Aurora environments.

Data Integration and Processing:

Architect data integration solutions for combining data from various on-premise and cloud-based systems.

Design and implement ETL/ELT workflows utilizing AWS Glue, Lambda, and other relevant AWS tools to transform raw data into consumable formats.

Work with stakeholders to identify data sources and design appropriate interfaces and APIs for seamless integration into the cloud architecture.

Governance and Security:

Ensure that data architecture adheres to best practices for data security, governance, and compliance (e.g., GDPR, HIPAA).

Define and implement data privacy and access control policies using AWS Identity and Access Management (IAM), encryption, and audit logging.

Establish data lineage and metadata management strategies to ensure transparency and traceability in data workflows.

Collaboration and Leadership:

Act as a technical leader and provide guidance on cloud-based data solutions to internal teams, including Data Engineers, Data Scientists, and DevOps engineers.

Collaborate with business stakeholders and leadership to understand requirements and design data solutions that meet business needs.

Lead the development of best practices and standards for AWS-based data engineering solutions and ensure these are followed across the organization.

Innovation and Optimization:

Continuously evaluate and introduce new AWS data technologies, tools, and features to optimize performance, scalability, and cost-efficiency.

Stay up-to-date with the latest trends in cloud data architecture and AWS service updates to recommend improvements and innovations in existing solutions.

Documentation and Knowledge Sharing:

Create and maintain clear and detailed documentation for data architecture, design decisions, and system configurations.

Promote a culture of knowledge sharing by mentoring team members and providing ongoing training on AWS data technologies.