Responsibilities
Responsibilities:Qualifications
- Independently complete conceptual, logical and physical data models for any supported platform, including SQL Data Warehouse, EMR, Spark, Data Bricks, Snowflake, Azure Synapse or other Cloud data warehousing technologies.
- Governs data design/modeling documentation of metadata (business definitions of entities and attributes) and constructions database objects, for baseline and investment funded projects, as assigned.
- Provides and/or supports data analysis, requirements gathering, solution development, and design reviews for enhancements to, or new, applications/reporting.
- Supports assigned project contractors (both on- & off-shore), orienting new contractors to standards, best practices, and tools.
- Advocates existing Enterprise Data Design standards; assists in establishing and documenting new standards.
- Contributes to project cost estimates, working with senior members of team to evaluate the size and complexity of the changes or new development.
- Ensure physical and logical data models are designed with an extensible philosophy to support future, unknown use cases with minimal rework.
- Develop a deep understanding of the business domain and enterprise technology inventory to craft a solution roadmap that achieves business objectives, maximizes reuse.
- Partner with IT, data engineering and other teams to ensure the enterprise data model incorporates key dimensions needed for the proper management: business and financial policies, security, local-market regulatory rules, consumer privacy by design principles (PII management) and all linked across fundamental identity foundations.
- Drive collaborative reviews of design, code, data, security features implementation performed by data engineers to drive data product development.
- Assist with data planning, sourcing, collection, profiling, and transformation.
- Create Source To Target Mappings for ETL and BI developers.
- Show expertise for data at all levels: low-latency, relational, and unstructured data stores; analytical and data lakes; data streaming (consumption/production), data in-transit.
- Develop reusable data models based on cloud-centric, code-first approaches to data management and cleansing.
- Partner with the data science team to standardize their classification of unstructured data into standard structures for data discovery and action by business customers and stakeholders.
- Support data lineage and mapping of source system data to canonical data stores for research, analysis and productization.
Qualifications:
- 12+ years of overall technology experience that includes at least 6+ years of data modelling and systems architecture.
- 6+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
- 6+ years of experience developing enterprise data models.
- 6+ years in cloud data engineering experience in at least one cloud (Azure, AWS, GCP).
- 6+ years of experience with building solutions in the retail or in the supply chain space.
- Expertise in data modelling tools (ER/Studio, Erwin, IDM/ARDM models).
- Fluent with Azure cloud services. Azure Certification is a plus.
- Experience scaling and managing a team of 5+ data modelers
- Experience with integration of multi cloud services with on-premises technologies.
- Experience with data profiling and data quality tools like Apache Griffin, Deequ, and Great Expectations.
- Experience with at least one MPP database technology such as Redshift, Synapse, Teradata, or Snowflake.
- Experience with version control systems like GitHub and deployment & CI tools.
- Experience with Azure Data Factory, Databricks and Azure Machine learning is a plus.
- Experience of metadata management, data lineage, and data glossaries is a plus.
- Working knowledge of agile development, including DevOps and DataOps concepts.
- Familiarity with business intelligence tools (such as PowerBI).
Skills, Abilities, Knowledge
- Excellent communication skills, both verbal and written, along with the ability to influence and demonstrate confidence in communications with senior level management.
- Proven track record of leading, mentoring, hiring and scaling data teams.
- Strong change manager. Comfortable with change, especially that which arises through company growth.
- Ability to understand and translate business requirements into data and technical requirements.
- High degree of organization and ability to manage multiple, competing projects and priorities simultaneously.
- Positive and flexible attitude to enable adjusting to different needs in an ever-changing environment.
- Strong leadership, organizational and interpersonal skills; comfortable managing trade-offs.
- Foster a team culture of accountability, communication, and self-management.
- Proactively drives impact and engagement while bringing others along.
- Consistently attain/exceed individual and team goals
- Ability to lead others without direct authority in a matrixed environment.
Differentiating Competencies Required
- Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies
- Lead technical discussions, driving clarity of complex issues/requirements to build robust solutions
- Strong communication skills to meet with business, understand sometimes ambiguous, needs, and translate to clear, aligned requirements
- Able to work independently with business partners to understand requirements quickly, perform analysis and lead the design review sessions.
- Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business.
- Places the user in the center of decision making.
- Teams up and collaborates for speed, agility, and innovation.
- Experience with and embraces agile methodologies.
- Strong negotiation and decision-making skill.
- Experience managing and working with globally distributed teams.
