Enterprise Data Architect Lead

7 days ago


Lahore, Punjab, Pakistan Tkxel LLC Full time
About Tkxel LLC

Tkxel is a leading software development company based in Reston, Virginia. Our team is committed to delivering innovative software solutions for top enterprises worldwide, helping them grow their businesses through cutting-edge technology.

Job Summary

We are seeking an experienced Enterprise Data Architect to lead the design, development, and maintenance of our enterprise data platform. The successful candidate will collaborate with data engineers, BI engineers, and data business analysts across various platform layers, ensuring a scalable, secure, and high-performance data infrastructure.

Key Responsibilities
  • Lead the development of a comprehensive roadmap for the enterprise big data platform, focusing on scalability, reliability, and cost efficiency.
  • Design and implement end-to-end data solutions that meet business intelligence and analytics needs while ensuring best practices in security, compliance, and operational efficiency.
  • Lead and mentor a team of data engineers, BI engineers, and cloud architects to implement high-quality, production-ready solutions.
  • Create and maintain modern data architectures, including data lakes, data warehouses, and real-time streaming solutions, leveraging AWS and Azure technologies.
  • Develop and optimize scalable, near real-time data ingestion pipelines, ensuring low-latency, high-throughput data processing for analytics and operational reporting.
  • Design and manage data streaming solutions using technologies such as Kafka, Kinesis, Apache Flink, or Spark Streaming, ensuring fault tolerance, consistency, and efficiency in real-time workflows.
  • Evaluate and ensure data security, governance, and compliance across all platforms, integrating IAM, encryption, access control, and data lineage tracking.
  • Collaborate with stakeholders to define and implement data integration strategies, including ETL, API-based data exchange, and event-driven architectures.
  • Drive automation and DevOps best practices for data pipeline deployment and monitoring, using tools such as Terraform, CloudFormation, and CI/CD pipelines.


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