Data - Architect

Roles and Responsibility

Data Architecture and Design

• Design and implement scalable, high-performance data architecture solutions to support analytics, reporting, and data science requirements.
• Create and maintain data models, schemas, and blueprints for cloud platforms.
• Define and enforce data governance, quality, and security standards across all platforms.

Cloud and Data Engineering

• Develop and maintain ETL/ELT pipelines for extracting, transforming, and loading data using tools like Databricks, AWS Glue, and AWS Lambda.
• Leverage AWS services such as S3, Redshift, Athena, Dynamo DB, and EMR for data storage and processing.
• Optimize the performance of data pipelines and ensure the availability of data systems.

Analytics and Reporting

• Collaborate with analytics and business intelligence teams to integrate data sources and enable advanced reporting.
• Implement and manage reporting transforming from existing Qlik to Sigma Enterprise BI to provide intuitive dashboards and insights.
• Partner with stakeholders to understand business requirements and translate them into data and analytics solutions.

Collaboration and Leadership

• Work closely with cross-functional teams, including Data Scientists, Analysts, and Developers, to ensure seamless data flow and accessibility.
• Mentor junior team members in best practices for data architecture and engineering.
• Stay up-to-date with emerging technologies and advocate for the adoption of innovative solutions.

Skills Qualification

• 12+ Years of relevant experience
• Bachelors or Master s degree in Computer Science, Data Science, or a related field.
• AWS certifications (e.g., AWS Certified Data Analytics - Specialty, AWS Certified Solutions Architect).
• Prior experience in implementing real-time data pipelines and streaming solutions.
• Familiarity with machine learning workflows and tools.

Primary Skill(s): Data Architect

Technical Skills

• Proficiency with AWS Cloud Services (e.g., S3, Redshift, Glue, Lambda, Athena, DynamoDB).
• Hands-on experience with Databricks for data engineering and analytics workloads.
• Expertise in designing and building data pipelines and workflows using tools like Apache Spark and Databricks.
• Strong understanding of relational databases, data lakes, and data warehousing concepts.
• Experience with Qlik, Sigma or other reporting tools like Tableau, Power BI, or Looker.

Programming and Frameworks

• Advanced proficiency in Python, SQL, and Scala for data manipulation and processing.
• Experience with infrastructure-as-code tools like Terraform or Cloud Formation.
• Knowledge of big data technologies such as Apache Hadoop, Kafka, or Flink is a plus.

Soft Skills

• Excellent problem-solving and analytical skills.
• Strong written and verbal communication skills to effectively present complex concepts.
• Ability to work in a fast-paced environment and manage multiple projects simultaneously.