Data Architecture

Data Strategy and Modeling:

- Align data strategy to business goals to support a mix of business strategy, improved decision-making, operations efficiency, and risk management.
- Design and implement data models that cater to scalability, performance, and business requirements.
- Define, design, and implement enterprise master data management strategy including data governance, quality, compliance and stewardship.
- Define, design and enforce data standards, guidelines, policies and processes to ensure data security, access, integrity, privacy, compliance, consistency, completeness, accuracy, permissions and provisioning.
- Data Solution Architecture:

- Design and build reliable, efficient and scalable data architecture to be used by the organization for all data solutions.
- Create robust, high-performance, and adaptable solutions optimized for modern platforms (Big Data, Relational, No-SQL, OLAP).
- Integrate technical functionality seamlessly.
- Implement and maintain scalable architectural data patterns, solutions and tooling to support business strategy
- Data Pipeline and ETL:

- Assemble large, complex data sets that meet functional and non-functional business requirements.
- Build infrastructure for optimal data extraction, transformation, and loading using technologies like Hadoop, Spark, and Kafka.
- Design, build, and launch shared data services and APIs to support and expose data-driven solutions in line with enterprise architecture standards
- Analytics and Insights:

- Develop analytical tools to extract actionable insights from our data pipeline.
- Provide key business performance metrics, including operational efficiency and customer acquisition.
- Technical Standards and Governance:

- Define technical principles, vision, and standards for our data warehouse.

Ensure data quality, security, and compliance
- Demonstrated ability to design and implement scalable data solutions.
- Proven track record of delivering high-quality results in a fast-paced environment.
- Proficiency in programming languages (Python, Java, or Scala).
- Familiarity with big data technologies (Hadoop, Spark, Kafka).
- Knowledge of both relational (SQL, PostgreSQL) and non-relational (MongoDB, Cassandra) database management systems.
- Strong problem solving skills
- Strong communication skills, both written and verbal. Able to
- solutions that are flexible and adaptable to clients’ needs
- Cutting edge innovator who continually studies new technologies and functionality, and is involved in projects that push the capabilities of existing technologies

Strong potential to continue to grow in career to higher levels of Services or other positions at Salesforce
- Education: Bachelor’s or College Degree in Computer Science, Engineering, Technical, Mathematics, Information Technology, Information Systems, Business, Education, Computer Engineering, or Architecture.
- Experience: 7+ years of experience in data engineering or related roles.

Certifications: Relevant certifications such as AWS, CDMP, MDM, DBA, SQL, SAP, TOGAF, API, or CISSP are advantageous.
- **Data Quality and Integrity**:Monitor the quality and accuracy of data input and output across CRM platform solution and integration to ensure reliable data quality for reporting and decision-making.
- **Sustainability data platform**:Measure the percentage of incident of data quality across platform. Proactively and preventively action to reduce the incident ratio of data quality.
- **Process Efficiency Improvements**:Track the identification and implementation of data strategy across CRM and integrated platform that reduce waste and improve effectiveness of transformed data to impact speed of business decision making on the right data at the right time.
- **Report and Dashboard Effectiveness**:Evaluate the creation and utilization of meaningful reports and dashboards to provide actionable insights and support informed decision-making for the sales team.

**Alignment with Ford Behaviors of Excellence, Focus and Collaboration**: