Azure Architect

Job Role: Azure Data Architect Location: Washington DC (2 days onsite role) Duration: Long term contract

NO H1B
Need experience in Databricks
Solutioning plus some experience in Data Protection (Immuta, Privacera, or
similar.) and/or Data Quality platforms (DQ Labs, Collibra DQ or similar).
Technical Skills:

• Data Modeling and Design: Proficiency in various data modeling techniques
  (e.g., relational, dimensional, NoSQL) and designing efficient and
  scalable database schemas.

• ETL/ELTProcesses: Expertise in designing, implementing, and optimizing data
  extraction, transformation, and loading/transferring processes using
  various tools and frameworks (e.g. Azure Data Factory, Google Cloud
  Dataflow).

• DataWarehousing and Data Lakes: Understanding of data warehousing principles,
  architecture, and best practices, as well as experience in designing and
  implementing data lakes using technologies like Hadoop, Spark, and cloud
  storage solutions (e.g. Azure Data Lake Storage). Deep understanding of
  Open Storage Formats such as UNIFORM, DELTA, ICEBERG

• DataLakehouse: Understanding of the Lakehouse concepts, principles and have
  practical experience with Databricks, Unity Catalog, Notebooks, Azure
  DevOps integration of Databricks for CI/CD. Familiarity with DBT

• BigData Technologies: Familiarity with big data processing frameworks
  like Apache Spark, Hadoop, Flink, and related ecosystems (e.g., Kafka,
  Hive, Pig).

• CloudPlatforms: Strong understanding of Azure and its data-related services
  (e.g., storage, databases, analytics, machine learning).

• Programmingand Scripting: Proficiency in one or more programming languages commonly
  used in data engineering and analysis (e.g., Python, SQL, Scala, Java).

• DataGovernance and Security: Knowledge of data governance principles, data
  quality management, data security best practices, and compliance
  requirements.

• DataIntegration Technologies: Experience with various data integration
  patterns and tools, including API integration, message queues, and data
  virtualization (e.g. Tibco DV, Denodo, etc.).

• BusinessIntelligence (BI) and Analytics: Understanding of BI concepts, reporting
  tools (e.g., Tableau, Power BI), and analytical techniques. Machine
  Learning (ML) Fundamentals: Basic understanding of machine learning
  concepts, algorithms, and platforms can be beneficial, especially for
  designing data pipelines that support ML initiatives.

• Infrastructure as Code (IaC): Familiarity with IaC tools like Terraform or
  CloudFormation for automating the provisioning and management of data
  infrastructure.
  Soft Skills:

• Communication: Excellent verbal and written communication skills to effectively convey
  complex technical concepts to both technical and non-technical
  stakeholders.

• Problem-Solving: Strong analytical and problem-solving skills to identify root causes of
  issues and design effective solutions.

• Leadership and Influence: Ability to lead technical discussions, influence
  decision-making, and guide development teams.

• Collaboration and Teamwork: Ability to work effectively in cross-functional teams,
  collaborating with data engineers, data scientists, business analysts, and
  other stakeholders.

• Critical Thinking: Ability to evaluate different technical options, assess
  trade-offs, and make informed architectural decisions.

• Business Acumen: Understanding of business goals and objectives to align data
  solutions with business needs.

• Strategic Thinking: Ability to think strategically about the long-term data
  architecture and its evolution.

• Adaptability and Learning Agility: Willingness to learn new technologies and adapt to
  changing business requirements and technological landscapes.
  Presentation Skills: Ability to present technical solutions and
  recommendations clearly and concisely to various audiences.

• Stakeholder Management: Ability to build and maintain strong relationships with
  stakeholders, understand their needs, and manage expectations.

• Time Management and Organization: Ability to manage multiple tasks, prioritize
  effectively, and meet deadlines.