Data Solutions Architect

Position Summary:

Title: Architect Premium III - Data Solutions Architect

Duration: 12 Months - Long Term

Location: Washington, DC 20433

Hybrid Onsite: 4 Days per week onsite from Day1.

Data Solutions Architect

Technical Skills:

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

ETL/ELT Processes: 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).

Data Warehousing 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

Data Lakehouse: 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

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

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

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

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

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

Business Intelligence (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.

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.