AWS Data Architect

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