Data Architect

4 weeks ago

Bengaluru, Karnataka, India Trianz Full time
Job Title: Data Architect

Trianz is a leading-edge technology platforms and services company that accelerates digital transformations at Fortune 100 and emerging companies worldwide in data & analytics, digital experiences, cloud infrastructure, and security. The company's "IP Led Transformations" vision, strategy, and business model are based on insights from a recent global study spanning 20+ industries and 5000+ companies worldwide.

Trianz believes that companies around the world face three challenges in their digital transformation journeys - shrinking 'time to transform' due to competition & AI, lack of digital-ready talent, and uncertain economic conditions. To help clients leapfrog over these challenges, Trianz has built IP and platforms that have transformed the adoption of the cloud, data, analytics & insights AI.

As a Data Architect at Trianz, you will be responsible for architecting secure, scalable, and highly performant data engineering and management solutions, including data warehouses, data lakes, ELT/ETL, and real-time data engineering/pipeline solutions. You will support the Principal Data Architect in defining and maintaining practice reference data engineering and data management architectures.

Key Responsibilities:

  • Architectural Design: Design and manage scalable end-to-end data solutions leveraging native Databricks capabilities, including Delta Live Tables, Apache Spark, Structured Streaming, Notebooks, Git CI/CD, Databricks' supported file types, and Libraries.
  • Databricks Implementation: Design and manage scalable end-to-end data solutions leveraging native Databricks capabilities, including Databricks SQL, best practice data modeling, Delta Lake, Delta Live tables, incremental and streaming workloads, table history, file management, API utilization, and Databricks Data Unity Catalog.
  • Hyperscaler Design: Competently leverage data-related cloud platform (AWS or Azure or GCP) capabilities to architect and develop end-to-end data engineering and data management solutions.
  • Client Engagement: Regular collaboration and partnership with clients to understand their challenges and needs, then translate requirements into data solutions that drive customer value.
  • Data Modeling: Create and maintain conceptual, logical, and physical data models that support both transactional and analytical needs, ensuring data models are optimized for performance and scalability.
  • Creativity: Be an out-of-the-box thinker and passionate about applying your skills to new and existing solutions alike, while always demonstrating a customer-first mentality.

Requirements:

  • 12+ years hands-on data solution architecture and implementation experience on modern cloud platforms (AWS preferred), including microservice and event-driven architectures.
  • Databricks Professional Data Engineer certification.
  • Databricks Platform Architect accreditation (AWS or Azure).
  • Databricks Platform Administrator accreditation.
  • Hands-on experience with Databricks Lakehouse implementations, Delta Live Tables, and building scalable data pipelines, including real-time (Kafka, Kinesis) in addition to Databricks' Structured Streaming.
  • Demonstrated, holistic experience and competence with Databricks concepts, including Delta tables, Apache Spark, Structured Streaming, Notebooks, Git CI/CD, Libraries, and Databricks SQL.
  • An architectural certification on either AWS, Azure, or GCP.
  • Experience with ML orchestration tools, such as MLflow and Kubeflow.
  • Containerization proficiency (Docker, Kubernetes) to deploy data pipelines and models.
  • Practical experience with end-to-end data engineering and data management supporting functions, including data modeling (conceptual, logical & physical), BI & analytics, data governance, data quality, data security/privacy/compliance, IAM, performance optimization.
  • Advanced SQL and data profiling.
  • Python or Scala.
  • Strong communication skills with the ability to convey technical concepts to non-technical users.
  • Strong, self-management skills demonstrating ability to multitask and self-manage goals and activities.

Additional/Nice-to-have Qualifications:

  • Databricks ML Engineer certification (Associate or Professional).
  • Databricks Generative AI Engineer certification.
  • Databricks Apache Spark Developer certification.
  • Any additional Databricks accreditations or certifications.

Apply now by sharing your updated resume with the below details:

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