Data Solutions Architect

About Us

Kimberly-Clark Corporation is a leading global health and hygiene company that has been making life better for people around the world for 150 years. Our iconic brands, such as Huggies, Kleenex, and Kotex, are used by millions of people every day.

We are driven by our purpose to make a positive impact on society while delivering value to our customers and shareholders. Our talented team of professionals is passionate about innovation, growth, and impact, and we are committed to driving excellence in all aspects of our business.

Job Description

As a Senior Data Engineer, you will play a key role in designing and implementing enterprise data solutions on cloud platforms. Your expertise in architecting scalable and secure data solutions will ensure that they meet our enterprise standards and requirements.

You will work closely with our cross-functional analytics team to generate insights from connected data, collaborating with data scientists, analysts, and other team members to translate business requirements into technical solutions. You will also manage and create data inventories for analytics and APIs to be consumed, ensuring that they are accessible and usable by various stakeholders.

Main Responsibilities:

• Design and operationalize enterprise data solutions on Cloud Platforms: Develop and implement scalable and secure data solutions on cloud platforms, ensuring they meet enterprise standards and requirements.
• Integrate Azure services, Snowflake technology, and other third-party data technologies: Seamlessly integrate various data technologies, including Azure services, Snowflake, and other third-party tools, to create a cohesive data ecosystem.
• Build and maintain high-quality data pipelines for analytic solutions: Develop robust data pipelines that automate the extraction, transformation, and loading (ETL) of data from various sources into a centralized data warehouse or lake.
• Collaborate with a multidisciplinary agile team to generate insights from connected data Work closely with data scientists, analysts, and other team members in an agile environment to translate business requirements into technical solutions.
• Manage and create data inventories for analytics and APIs to be consumed: Develop and maintain comprehensive data inventories that catalog available data assets and their metadata.
• Design data integrations with internal and external products: Architect and implement data integration solutions that enable seamless data exchange between internal systems and external partners or products.
• Build data visualizations to support analytic insights: Create intuitive and insightful data visualizations using tools like PowerBI to help stakeholders understand complex data sets and derive actionable insights.

Key Qualifications and Experiences:

• 5+ years of experience tailoring, configuring, and crafting solutions within the Snowflake environment, including a profound grasp of Snowflake's data warehousing capabilities, data architecture, SQL optimization for Snowflake, and leveraging Snowflake's unique features such as Snowpipe, Streams, and Tasks for real-time data processing and analytics.
• 3+ years demonstrated expertise in architecting solutions within the Snowflake ecosystem, adhering to best practices in data architecture and designs.
• 10+ years of data engineering or design experience, designing, developing, and deploying scalable enterprise data analytics solutions from source system through ingestion and reporting.
• Expertise in data modeling principles/methods including, Conceptual, Logical & Physical Data Models for data warehouses, data lakes and/or database management systems.
• 5+ years of hands-on experience designing, building, and operationalizing data solutions and applications using cloud data and analytics services in combination with 3rd parties.
• 10+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols).
• 10+ years of experience with database development and scripting.
• Deep understanding of data architecture, data engineering, data warehousing, data analysis, reporting, and data science techniques and workflows: You should have a comprehensive knowledge of designing and implementing data systems that support various analytic and operational use cases, including data storage, processing, and retrieval.
• Skilled in creating data products that support analytic solutions: Proficiency in developing data products that enable stakeholders to derive meaningful insights and make data-driven decisions.
• Proficiency in working with APIs and understanding data structures to serve them: Experience in designing, developing, and consuming APIs for data access and integration.
• Experience with Object-Relational Mapping (ORM) frameworks: Familiarity with ORM frameworks, such as Hibernate or Entity Framework, to efficiently map data between relational databases and application code.
• Knowledge of managing sensitive data, ensuring data privacy and security: Expertise in handling sensitive data with strict adherence to data privacy regulations and security best practices to protect against unauthorized access and breaches.
• Expertise in data visualization tools, specifically PowerBI: Proficiency in using data visualization tools like PowerBI to create interactive and insightful dashboards and reports that effectively communicate complex data insights.
• Strong problem-solving skills and ability to work as part of a technical, cross-functional analytics team: Excellent analytical and troubleshooting abilities, with the capability to collaborate effectively with team members from various technical and business domains.
• Experience with relational and non-relational databases (NoSQL, graph databases, etc.): Solid experience in working with different types of databases, including traditional relational databases (e.g., SQL Server, MySQL) and non-relational databases (e.g., MongoDB, graph databases).
• Agile learner with a passion for solving complex data problems and delivering insights: A proactive and continuous learner with enthusiasm for addressing challenging data issues and providing valuable insights through innovative solutions.
• Proficiency in programming languages such as SQL, NoSQL, Python, Java, R, and Scala: Strong coding skills in multiple programming languages used for data manipulation, analysis, and pipeline development.
• Familiarity with relational and non-relational databases, including GraphQL and MongoDB: In-depth understanding of both relational (SQL-based) and non-relational (NoSQL) databases, with specific experience in technologies like GraphQL and MongoDB.
• Experience with ETL (extract, transform, and load) systems and API integrations: Expertise in building and maintaining ETL processes to consolidate data from various sources into centralized repositories, and integrating APIs for seamless data exchange.
• Understanding of data storage solutions, knowing when to use a data lake versus a data warehouse: Knowledge of different data storage architectures and the ability to choose the appropriate solution (data lake or data warehouse) based on specific use cases and data characteristics.
• Ability to write scripts for automation and repetitive task management: Proficiency in scripting languages (e.g., Python, Bash) to automate data processing tasks and reduce manual efforts.
• Basic understanding of machine learning concepts to support data scientists on the team: Familiarity with key machine learning principles and techniques to better collaborate with data scientists and support their analytical models.
• Proficiency with big data tools such as Hadoop, MongoDB, and Kafka: Experience in using big data technologies to manage, process, and analyze large datasets efficiently.
• Knowledge of cloud computing, including cloud storage, product portfolios, and pricing models (Azure): Understanding of cloud platforms and services, particularly Azure, including storage options, available tools, and cost considerations.
• Experience in data security, ensuring data is securely managed and stored to protect it from loss or theft while maintaining compliance: Strong background in implementing security measures to safeguard data and comply with regulatory requirements.
• Bachelor's degree in management information systems/technology, Computer Science, Engineering, or related discipline. MBA or equivalent is preferred.
• Minimum 10+ years of experience in designing large-scale data solutions, performing design assessments, crafting design options and analysis, finalizing preferred solution choice working with IT and Business stakeholders.