Staff Data Engineer-Data Modeling (Austin, San Antonio, or Dallas)

Responsibilities

Since H-E-B Digital Technology's inception, we've been investing heavily in our customers' digital experience, reinventing how they find inspiration from food, make food decisions, and ultimately get food into their homes. This is an exciting time to join H-E-B Digital-we're using the best available technologies to deliver modern, engaging, reliable, and scalable experiences to meet the needs of our growing audience.As a Staff Data Engineer, you'll lead, coach, and mentor engineers and teams and provide technical direction and support. You'll collaborate with Product, Data Science, Application, and Analytics teams to develop a clear understanding of data and data infrastructure needs to resolve data-related technical issues and ensure optimal data design and efficiency.Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources--People--drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company.Do you have a:HEART FOR PEOPLE... ability to serve as thought leader and mentor to Partners?HEAD FOR BUSINESS... you make sound, mature technical judgments that result in significant impact?PASSION FOR RESULTS... you can deliver sweeping technical initiatives with minimal guidance?

We are looking for: 

• 7+ years of related experience to data engineering and data modeling; retail experience preferred  

 

What is the work? Data Modeling:  

• Design/Develop/Maintain data models (including conceptual, logical, and physical models) that support application and analytical systems 

• Leverage data modeling software to create optimized data designs that facilitate consistency, reuse and ensure efficient storage and retrieval and limit data duplications  

• Use data modeling to create a common language with visual references to describe data and its relationships across business domains.  

• Define structures, relationships, data types, and constraints to reduce quality issues 

• Create data models to make it easier to understand data relationships, identify duplication, and align on sources of truth 

• Publish data models and support data modeling questions and education 

• Collaborate with data engineers across one or more teams to deliver initiatives 

• Works with Product, Business, and Analyst stakeholders to understand business processes and its relationship to data  

• Mentors / provides support to junior data engineers; coaches / mentors engineers in data modeling techniques, processes, new technologies 

 

Data Design & Development: 

• Leads design / development / maintenance of largescale data pipelines; may assist in diagnosing / solving complex production support issues 

• Provides operational and technical expertise to design algorithms patterns that support large/complex datasets for analytics, implement calculations, cleanse data, ensure standardization of data, and map / link data from more than one source 

• Performs full SDLC process, including planning, design, development, certification, implementation, and support on more complex projects 

• Establishes team operational plans; develops / implements new processes, standards, and operational plans that impact results; develops technical roadmaps 

• Makes recommendations for overall data platforms, workflow, design, architecture, security, scalability, reliability, and performance 

• Recommends changes to processes, tools at the group / dept level based on industry standards, patterns, and practices 

• Collaborates with external technical teams to ensure timely, high-quality solutions 

• Engages with shared services teams and vendors as needed 

• Influences others in technical decision-making / technology adoption in assigned domain 

  

What is your background? 

• Advanced knowledge in data modeling techniques such as Entity Relationship, Database Normalization, Dimensional (Star, Snowflake), Data Vault 

• Experience with data modeling software (such as Erwin, DbSchema, Lucid, ER/Studio) 

• Experience with cloud technologies such as GCP, Azure, or AWS 

• Experience with database technologies such as Oracle, Teradata, or Postgres 

• Experience with common data engineering tools (e.g., Git, Jira, Confluence) 

• Proficient in SQL and Python  

• A related degree or comparable formal training, certification, or work experience 

• 7+ years of experience related to data engineering and data modeling; retail experience preferred 

• Experience in team leadership 

• Experience working in large scale infrastructure, large data sets, and mission critical SLAs 

 

Do you have what it takes to be an H-E-B Staff Data Engineer? 

• High degree of personal accountability to self and team for continued growth 

• Able to drive progress without having a complete picture and can articulate potential tradeoffs and prioritize when faced with ambiguity. 

• Comprehensive knowledge of Lean Startup / Agile development methodologies 

• Knowledge of business intelligence, analytics / reporting, and application integration 

• Knowledge of data architectures such as data warehouse, data lake, and data mesh and when to apply 

• Expert understanding of coding standards, design principles / patterns, and data architecture and data modeling best practices and guidelines for different data and analytic platforms 

• Advanced verbal / written communication and data presentation skills 

• Strong prioritization skills 

• Ability to deliver on ambiguous projects with incomplete information 

• Ability to act as a thought leader and mentor to junior team members 

• Ability / willingness to learn new technologies as they emerge 

• Ability to calmly work under pressure 

• Ability to work a flexible schedule as needed 

• Ability to collaborate across multiple work locations 

• Ability to work within a team, and willingness to take feedback from peers and mentors 

Can you… 

• Function in a fast-paced environment 

• Work extended hours; sit for extended periods 

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