Role Name: Data Architect (Data Modelling)
Work site: Atlanta, US (Onsite)
Long term
Job Description
We are looking for an experienced Data Architect / Data Engineer to design, develop, and implement scalable enterprise data solutions.
The candidate will work closely with business stakeholders, application teams, data engineers, analytics teams, and cloud/platform teams to translate business requirements into robust data architectures and engineering solutions.
The role requires a strong combination of data architecture, data modeling, ETL/data integration, cloud data platforms, SQL, data warehousing, and data governance expertise.
Key Responsibilities
Data Architecture & Modelling
Design scalable and secure enterprise data architectures aligned with business requirements.
Develop conceptual, logical, and physical data models.
Design solutions for data warehouses, data lakes and lakehouse architectures.
Define data architecture principles, standards, patterns, and best practices.
Evaluate existing data architecture and recommend modernization opportunities.
Design solutions for structured, semi-structured, and unstructured data.
Data modeling, governance, integration frameworks, security, scalability, and translating business requirements into technical solutions are commonly identified as core Data Architect responsibilities.
Data Engineering & Integration
Design, develop, and maintain scalable ETL/ELT data pipelines.
Integrate data from multiple internal and external source systems.
Develop data transformation and processing solutions for batch and streaming workloads.
Implement reusable frameworks for data ingestion, transformation, validation, and publishing.
Optimize data pipelines and database queries for performance and scalability.
Perform data profiling, cleansing, reconciliation, and data-quality validation.
Troubleshoot data pipeline failures and production data issues.
Building and maintaining pipelines, databases, storage systems, and optimizing data processing are core Data Engineer responsibilities.
Cloud & Data Platform
Design and implement cloud-based data solutions using Google Cloud Platform, AWS, or Azure.
Work with cloud data warehouses, data lakes, object storage, and distributed processing platforms.
Support migration and modernization of legacy/on-premise data platforms to cloud-based architectures.
Design solutions considering scalability, reliability, security, performance, and cost.
Data Governance & Security
Implement enterprise data standards, metadata management, data lineage, and data-quality practices.
Ensure appropriate data access controls and security standards are incorporated into solution designs.
Partner with governance and business teams to establish consistent definitions and standards for enterprise data.
Governance, security, standards, and cross-team alignment are also established elements of the Data Architect role.
Required Technical Skills
Primary Skills
Data Architecture
Data Engineering
Data Modeling
SQL
ETL / ELT
Data Warehousing
Cloud Data Platforms: Google Cloud Platform / AWS / Azure
Data Lake / Lakehouse
Database design
Data Integration
Data Quality
Secondary / Preferred Skills
Informatica / IICS
Databricks
Snowflake
BigQuery
Spark / PySpark
Python
Kafka or other streaming technologies
Airflow / Cloud Composer
DBT
NoSQL databases
API-based data integration
Metadata and data catalog solutions
CI/CD and DevOps practices
Git
Agile / Scrum
Qualifications
Strong experience in data architecture, data engineering, or enterprise data integration.
Strong knowledge of relational databases, dimensional modeling, and modern cloud data architectures.
Hands-on experience designing and implementing complex data pipelines.
Strong SQL and data-analysis skills.
Experience translating business requirements into scalable technical solutions.
Strong understanding of OLTP vs. OLAP, normalized/3NF models, dimensional modeling, Star/Snowflake schemas, and data warehousing concepts.
Ability to perform architecture reviews and provide technical guidance to engineering teams.
Excellent analytical, problem-solving, documentation, and communication skills.
Ability to collaborate effectively with both technical and business stakeholders.
Preferred Experience
Enterprise-scale cloud migration or data modernization programs.
Legacy ETL/data warehouse modernization.
Real-time/streaming data architectures.
Data governance, metadata management, lineage, and data-quality frameworks.
Designing data platforms supporting BI, analytics, AI/ML, and GenAI use cases.
