About The Role

Lead the architecture, design, and implementation of enterprise-scale data platforms from project inception through production deployment.
Own technical delivery across multiple client engagements while ensuring high-quality engineering standards.
Define solution architecture, technical roadmaps, and implementation strategies aligned with client business goals.
Conduct architecture reviews, code reviews, and establish engineering best practices across project teams.
Mentor and coach Data Engineers while fostering technical excellence and continuous learning.
Serve as the primary technical leader for complex engineering initiatives and critical project decisions.

Partner directly with Fortune 500 clients to understand business requirements and translate them into scalable technical solutions.
Lead discovery workshops, architecture sessions, and technical planning meetings with both business and engineering stakeholders.
Present solution designs, delivery plans, and architectural recommendations to technical leadership and executive audiences.
Build trusted relationships with client teams while providing technical guidance throughout project execution.
Support pre-sales activities by contributing technical expertise, solution estimates, and implementation approaches when required.

Design, develop, and optimize enterprise-grade data pipelines using the Databricks Unified Analytics Platform.
Build scalable ETL and ELT frameworks capable of processing large-scale structured and unstructured datasets.
Design and implement Lakehouse architectures using Delta Lake and Medallion design patterns.
Develop high-performance Spark applications for batch and real-time data processing.
Integrate data from enterprise applications, APIs, streaming platforms, and cloud storage solutions.
Ensure data quality, integrity, and reliability through automated validation, testing, and monitoring.

Architect cloud-native data platforms across AWS, Azure, or Google Cloud Platform.
Implement Infrastructure-as-Code using Terraform or similar technologies.
Build and maintain CI/CD pipelines supporting automated testing and deployment.
Optimize cloud infrastructure for scalability, reliability, security, and cost efficiency.
Monitor platform performance and proactively resolve operational issues.

Implement enterprise data governance frameworks and security best practices.
Configure Unity Catalog, metadata management, lineage, and role-based access controls.
Ensure compliance with organizational security standards and regulatory requirements.
Promote data observability and operational excellence across production environments

Partner closely with Product Managers, Data Scientists, Analytics Engineers, Machine Learning Engineers, and Software Engineers to deliver high-impact data products.
Enable AI and machine learning initiatives through scalable feature engineering pipelines and production-ready datasets.
Contribute reusable frameworks, accelerators, and engineering standards that improve delivery across client engagements.

Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related technical discipline.
8+ years of experience designing and building enterprise-scale data platforms.
5+ years of hands-on experience with Databricks and Apache Spark.
Proven experience leading enterprise data engineering projects from architecture through production delivery.
Strong expertise in Python, SQL, and Spark for large-scale data processing.
Deep understanding of Delta Lake, Lakehouse architecture, and modern data platform design.
Experience working with AWS, Azure, or Google Cloud Platform.
Strong knowledge of ETL/ELT frameworks, distributed computing, and data modeling.
Experience implementing CI/CD pipelines and Infrastructure-as-Code.
Strong understanding of data governance, security, metadata management, and data quality practices.
Experience optimizing distributed data processing workloads for performance and cost.
Excellent communication and stakeholder management skills with experience working directly with enterprise clients.
Demonstrated ability to mentor engineers and lead technical initiatives

Databricks Certified Professional Data Engineer certification.
Experience with Delta Live Tables, MLflow, Unity Catalog, and Databricks SQL.
Experience with Kafka, Kinesis, Event Hubs, or other streaming technologies.
Hands-on experience with Snowflake, dbt, Airflow, or modern data orchestration tools.
Experience with Kubernetes, Docker, and Terraform.
Knowledge of AI/ML data platforms, Feature Stores, or Retrieval-Augmented Generation (RAG) architectures.
Previous consulting or professional services experience delivering solutions for enterprise clients.
Experience within retail, e-commerce, financial services, logistics, healthcare, or ad-tech environments.

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Senior Data Engineer