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SYSTEM ARCHITECT (DATA + MULESOFT + AWS )

Overview Of Role

Design and deliver scalable, enterprise-grade data solutions across AWS and Databricks, covering end-toend

architecture, ingestion, data modeling, transformation, orchestration, APIs, and downstream

integrations.

Lead the development and modernization of high-volume, business-critical data products and platforms,

translating complex business and technical requirements into secure, scalable, and maintainable

solutions.

Establish architectural standards, coding practices, reusable frameworks, and engineering best practices

to drive consistency, quality, and maintainability.

Define robust orchestration, monitoring, and operational patterns to ensure performance, scalability,

reliability, resilience, and cost optimization.

Provide hands-on technical leadership across solution design, architecture reviews, code reviews,

development, debugging, and complex technical decision-making.

Lead cross-functional delivery across Product, Business, Architecture, Data, API, QA, and Platform teams

within Agile environments.

Drive data quality, governance, observability, security, and production readiness across data products and

platforms.

Mentor engineers and technical leads while promoting engineering excellence and continuous

improvement.

Required Qualifications

10+ years of experience in Data Engineering / Data Architecture, including data modeling, database

design, ETL/ELT, data ingestion, legacy system migration, and processing large-scale datasets.

3+ years of hands-on experience with Databricks, Apache Spark, and PySpark, including Spark

performance optimization.

Strong hands-on experience with AWS services including S3, Lambda, Aurora/RDS, Step Functions, and

CloudWatch.

Strong SQL and Python development skills with proven experience in developing, debugging, and

optimizing data solutions.

Experience designing and implementing robust data orchestration workflows, including scheduling,

dependencies, retries, error handling, monitoring, and operational resilience.

Strong understanding of software engineering practices, including coding standards, code reviews,

testing, reusable frameworks, and development best practices.

Experience with REST APIs and API integrations, including request/response flows, integration patterns,

and downstream data integrations.

Working knowledge of MuleSoft or similar API and integration platforms.
Experience with CI/CD practices and tools such as Azure DevOps or equivalent platforms.
Strong understanding of data quality, validation, reconciliation, monitoring, and production support

practices.

Experience creating, reviewing, and maintaining functional and technical documentation.
Strong understanding of Agile methodologies, including sprint planning, backlog refinement, and iterative

delivery.

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Proven experience working with and leading cross-functional teams comprising Data Engineering, API, QA,

Architecture, Product, and Business stakeholders.

Proven experience leading, mentoring, and providing technical guidance to data engineers and technical

leads.

Excellent verbal and written communication skills, with the ability to effectively communicate complex

technical concepts to both technical and business stakeholders.

Preferred Qualifications

Understanding of AWS or similar cloud infrastructure optimization and cost management, including

evaluating compute, storage, and processing options.

Data engineer with API development experience
Experience with Redis or any in-memory database
Relevant AWS Certifications in cloud computing, data engineering, and/or solutions architecture.
8 + years of experience writing and maintaining infrastructure code.
8 + years of experience using any automation tools and ADO CI/CD pipelines