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.

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