Staff Data Engineer, Customer & Identity Resolution (Austin, TX On-Site)

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… you’re willing to facilitate solutions with multiple engineers, provide upward communication, and mentor others? HEAD FOR BUSINESS… you consistently demonstrate and uphold the standards of coding, infrastructure, and process? PASSION FOR RESULTS… you’re capable of high-velocity contributions in multiple technical domains? Key Responsibilities & Essential Functions: Leads team in designing / developing data integrations that support application engineering and system integration 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 Leads engineers across one or more squads to deliver initiatives Mentors / provides support to junior data engineers; coaches / mentors engineers in engineering techniques, processes, new technologies Builds / supports complex data pipelines, APIs, data integrations, data streaming solutions, and predictive model implementations Identifies complex data from upstream sources to enable new capabilities Designs and builds large-scale batch / real-time data pipelines with big data processing frameworks Architects monitoring capabilities based on business SLA and data quality Maintains / streamlines existing data pipelines end to end 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, work flow, design, architecture, security, scalability, reliability, and performance Recommends changes to processes, tools at the group / dept level based on industry standards, patterns, and practices Tests technical solutions to ensure data integrity and system functionality of own work, with consideration toward broader system, E2E Designs scalable, efficient data models to support data integration, storage, and retrieval across complex systems. Develops and implements data quality framework for data accuracy, consistency, and completeness across workflows. Works with Product, Business, and Analyst stakeholders to confirm data quality, discuss requirements, and support data testing Creates team documentation and training related to technology stacks and standards Diagnoses / troubleshoots extremely complex issues independently Performs data validation and quality assurance on own work and of junior engineers 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 Knowledge in machine learning concepts The responsibilities and essential functions outlined above describe the general nature and level of work assigned to this position. This is not an exhaustive list of all duties, responsibilities, and skills required. Duties and responsibilities may be modified at any time based on business needs. Employees may be required to perform other job-related tasks as requested by their supervisor, subject to reasonable accommodations. Qualifications & Key Requirements: Work Experience: 7+ years of experience related to data engineering Experience in team leadership Experience working in large scale infrastructure, large data sets, and mission critical SLAs Knowledge/Skills/Abilities: Customer Data and Identity Resolution: Hands-on experience architecting customer data platforms, Identity Resolution frameworks, and Customer 360 solutions. Proficient in designing deterministic and probabilistic matching strategies to resolve fragmented identifiers into unified customer profiles while maintaining identity graph stability. Cloud: Proven experience working with Google Cloud Platform (GCP) and managed services, including Cloud Dataflow (Apache Beam), Cloud Dataproc (Spark), and orchestration via Managed Service for Apache Airflow (Cloud Composer). Programming & Data Processing: Strong proficiency in Python, PySpark, and SQL for large-scale data manipulation and pipeline development. Data Architecture & Streaming: Deep understanding of modern Data Lakehouse architectures (Databricks, BigQuery), dimensional data modeling, scalable ETL/ELT pipelines, and real-time streaming using Kafka or Google Cloud Pub/Sub. Graph Database Engineering: Experience designing, modeling, and querying relationship analytics in graph databases. Proficient in Google Cloud Spanner Graph (operational traversals) and BigQuery Graph (analytical clustering). ML & Entity Resolution: Experience building and operationalizing machine learning pipelines for record linkage and entity resolution. Experience in training pairwise classical and probabilistic classification models to score match probabilities, utilizing vector embeddings (semantic similarity), and applying graph-based clustering algorithms. Data Modeling: Practical expertise in dimensional modeling and designing optimized, high-performance schemas. CI/CD & Engineering Best Practices: Experience with streamlined deployment workflows using version control via Git and establishing automated testing/CI/CD pipelines. Cross-Functional Collaboration: A track record of partnering across Product, Data Science, and Engineering teams to turn business objectives into resilient technical solutions. Nice-to-Have Qualifications: Domain Experience: Previous background in retail, e-commerce, or high-volume digital commerce environments. Probabilistic Matching Frameworks: Hands-on experience with Splink (Fellegi-Sunter methodology) for probabilistic record linkage and rule-based matching at scale. Graph Data Science & Algorithms: Experience applying graph algorithms (e.g., Connected Components for grouping matched records, Community Detection) to cluster, analyze, and partition massive identity graphs. Graph Ecosystems: Working knowledge of Neo4j and the Cypher / ISO GQL query language. Downstream Activation & CDPs: Familiarity with piping resolved customer profiles into CDPs or marketing platforms Education: A related degree or comparable formal training, certification, or work experience - Licenses/Certifications: Physical Demands & Working Conditions: Function in a fast-paced environment Work extended hours; sit for extended periods The work environment characteristics described here are representative of those a Partner encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Last revised: 11/01/2024 JDENGINEERING 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… you’re willing to facilitate solutions with multiple engineers, provide upward communication, and mentor others? HEAD FOR BUSINESS… you consistently demonstrate and uphold the standards of coding, infrastructure, and process? PASSION FOR RESULTS… you’re capable of high-velocity contributions in multiple technical domains? Key Responsibilities & Essential Functions: Leads team in designing / developing data integrations that support application engineering and system integration 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 Leads engineers across one or more squads to deliver initiatives Mentors / provides support to junior data engineers; coaches / mentors engineers in engineering techniques, processes, new technologies Builds / supports complex data pipelines, APIs, data integrations, data streaming solutions, and predictive model implementations Identifies complex data from upstream sources to enable new capabilities Designs and builds large-scale batch / real-time data pipelines with big data processing frameworks Architects monitoring capabilities based on business SLA and data quality Maintains / streamlines existing data pipelines end to end 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, work flow, design, architecture, security, scalability, reliability, and performance Recommends changes to processes, tools at the group / dept level based on industry standards, patterns, and practices Tests technical solutions to ensure data integrity and system functionality of own work, with consideration toward broader system, E2E Designs scalable, efficient data models to support data integration, storage, and retrieval across complex systems. Develops and implements data quality framework for data accuracy, consistency, and completeness across workflows. Works with Product, Business, and Analyst stakeholders to confirm data quality, discuss requirements, and support data testing Creates team documentation and training related to technology stacks and standards Diagnoses / troubleshoots extremely complex issues independently Performs data validation and quality assurance on own work and of junior engineers 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 Knowledge in machine learning concepts The responsibilities and essential functions outlined above describe the general nature and level of work assigned to this position. This is not an exhaustive list of all duties, responsibilities, and skills required. Duties and responsibilities may be modified at any time based on business needs. Employees may be required to perform other job-related tasks as requested by their supervisor, subject to reasonable accommodations. Qualifications & Key Requirements: Work Experience: 7+ years of experience related to data engineering Experience in team leadership Experience working in large scale infrastructure, large data sets, and mission critical SLAs Knowledge/Skills/Abilities: Customer Data and Identity Resolution: Hands-on experience architecting customer data platforms, Identity Resolution frameworks, and Customer 360 solutions. Proficient in designing deterministic and probabilistic matching strategies to resolve fragmented identifiers into unified customer profiles while maintaining identity graph stability. Cloud: Proven experience working with Google Cloud Platform (GCP) and managed services, including Cloud Dataflow (Apache Beam), Cloud Dataproc (Spark), and orchestration via Managed Service for Apache Airflow (Cloud Composer). Programming & Data Processing: Strong proficiency in Python, PySpark, and SQL for large-scale data manipulation and pipeline development. Data Architecture & Streaming: Deep understanding of modern Data Lakehouse architectures (Databricks, BigQuery), dimensional data modeling, scalable ETL/ELT pipelines, and real-time streaming using Kafka or Google Cloud Pub/Sub. Graph Database Engineering: Experience designing, modeling, and querying relationship analytics in graph databases. Proficient in Google Cloud Spanner Graph (operational traversals) and BigQuery Graph (analytical clustering). ML & Entity Resolution: Experience building and operationalizing machine learning pipelines for record linkage and entity resolution. Experience in training pairwise classical and probabilistic classification models to score match probabilities, utilizing vector embeddings (semantic similarity), and applying graph-based clustering algorithms. Data Modeling: Practical expertise in dimensional modeling and designing optimized, high-performance schemas. CI/CD & Engineering Best Practices: Experience with streamlined deployment workflows using version control via Git and establishing automated testing/CI/CD pipelines. Cross-Functional Collaboration: A track record of partnering across Product, Data Science, and Engineering teams to turn business objectives into resilient technical solutions. Nice-to-Have Qualifications: Domain Experience: Previous background in retail, e-commerce, or high-volume digital commerce environments. Probabilistic Matching Frameworks: Hands-on experience with Splink (Fellegi-Sunter methodology) for probabilistic record linkage and rule-based matching at scale. Graph Data Science & Algorithms: Experience applying graph algorithms (e.g., Connected Components for grouping matched records, Community Detection) to cluster, analyze, and partition massive identity graphs. Graph Ecosystems: Working knowledge of Neo4j and the Cypher / ISO GQL query language. Downstream Activation & CDPs: Familiarity with piping resolved customer profiles into CDPs or marketing platforms Education: A related degree or comparable formal training, certification, or work experience - Licenses/Certifications: Physical Demands & Working Conditions: Function in a fast-paced environment Work extended hours; sit for extended periods The work environment characteristics described here are representative of those a Partner encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Last revised: 11/01/2024 JDENGINEERING