Job Title: AWS Data Architect (Hands-on)
Location: Fort Mill, South Carolina (Hybrid)
Employment Type: Full-time
Experience Required: 15+ years

Role Overview
We are looking for a highly skilled, hands-on AWS Data Architect to design and implement
enterprise-grade data platforms for a leading financial services client. This role requires deep
technical expertise, strong architectural ownership, and the ability to build, optimize, and scale
modern data ecosystems on AWS.
The ideal candidate will not only define architecture but also actively design, code, and
troubleshoot pipelines and data systems.
Key Responsibilities
1. End-to-End Data Architecture (Hands-on)
 Design and implement scalable AWS-based data platforms including data lakes, lakehouses,
and warehouses.
 Build robust data pipelines (batch & real-time) from scratch.
 Define and enforce data architecture standards, frameworks, and governance models.
 Create logical and physical data models (OLTP, OLAP, dimensional modeling).
2. AWS Data Stack (Deep Expertise Required)
Hands-on experience designing and implementing solutions using:
 Storage & Warehousing:
Amazon S3, Redshift, Aurora, RDS
 Data Processing & ETL:
AWS Glue (Spark), EMR (PySpark), Lambda
 Streaming & Real-time:
Kinesis (Streams, Firehose), MSK (Kafka)
 Query & Analytics:
Athena, Redshift Spectrum
 Orchestration:
Step Functions, Airflow (MWAA preferred)
 Governance & Security:
Lake Formation, IAM, KMS, Secrets Manager
3. Advanced Data Engineering (Must be Hands-on)
 Develop complex ETL/ELT pipelines using Python, PySpark, SQL.
 Optimize large-scale distributed data processing systems.
 Implement CDC (Change Data Capture) and near real-time ingestion pipelines.
 Work with structured, semi-structured, and unstructured data.
4. Performance & Cost Optimization
 Tune Redshift/Spark workloads (partitioning, compression, distribution keys).
 Optimize storage & compute usage for cost efficiency.
 Troubleshoot performance bottlenecks across pipelines.
5. Financial Domain Data Expertise
 Design solutions aligned to regulatory, compliance, and audit requirements.
 Handle sensitive financial data (PII, PCI) securely.
 Ensure adherence to data governance, lineage, and auditability standards.
6. DevOps & Automation
 Implement Infrastructure as Code using Terraform / CloudFormation.
 Build CI/CD pipelines for data workflows.
 Automate deployments, monitoring, and alerting.
7. Collaboration & Leadership
 Work closely with data engineers, analysts, risk teams, and business stakeholders.
 Provide technical leadership and mentorship.
 Drive architectural decisions and best practices across teams.
Required Qualifications
Experience
 15+ years in data engineering / data architecture
 6–8+ years of strong AWS data architecture experience
 Proven track record of designing enterprise data platforms at scale
Technical Skills (Must Have)
 Strong programming: Python (mandatory), PySpark, SQL
 Deep hands-on with Spark, distributed processing frameworks
 Expertise in data warehousing & modeling concepts
 Experience with real-time and streaming architectures
 Hands-on experience with CI/CD + DevOps pipelines
AWS Expertise
 Strong knowledge of AWS Well-Architected Framework
 Experience building serverless data architectures
 Security best practices in financial cloud environments
Preferred Qualifications
 AWS Certifications:
o AWS Certified Solutions Architect (Professional preferred)
o AWS Certified Data Analytics – Specialty
 Experience with:
o Snowflake / Databricks (nice to have)
o Data quality frameworks (Great Expectations, Deequ)
o Data catalog tools (Glue Catalog, Collibra, Alation)
 Exposure to risk, compliance, or financial analytics systems
Soft Skills
 Strong problem-solving & analytical mindset
 Excellent communication with technical & business stakeholders
 Ability to work in a hybrid and cross-functional setup
 Leadership mindset with hands-on contribution
What Makes You a Great Fit
 You don’t just design—you build and optimize.
 You have experience handling highly regulated financial data systems.
 You can operate at architect level while staying deeply technical

AWS Data Architect

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