Data Analytics Engineer Jobs in Arkansas

Position Summary

The Decision Science End-to-End (E2E) team team serves Walmart Enterprise Operations. We collaborate across multiple business areas to accelerate data-driven decisions and build lasting analytical capabilities. Strong data foundations are at the core of how we work: we invest in getting the modeling, pipeline design, and data quality right, because everything our partners depend on is built on top of that layer. We are a lean team, and every member, regardless of seniority, wears multiple hats. Our Staff Engineers are no exception. You will be expected to move fluidly across architecture, hands-on engineering, mentorship, and partner-facing technical leadership, often within the same week. We pride ourselves on full ownership and build partner relationships grounded in trust. Our partners count on us not just to deliver; we speak up when something needs to change, before they have to ask.

Your Opportunity

We are seeking a Staff Data Engineer to serve as a senior technical leader on our team. You will set the technical direction for our data platform, define the standards the team builds against, and be the person others turn to when the problem is hard and the stakes are high. Critically, you will also be the person who makes sure the team never loses sight of data foundations: the SQL quality, the model integrity, the pipeline reliability, and the documentation discipline that everything else depends on. As part of Decision Science E2E, you will own the architecture of data solutions spanning multiple Walmart business formats (e.g. Walmart US, Sam's Club, Supply Chain). You will drive the design of scalable, observable, and maintainable data systems, mentor engineers across experience levels, and serve as a bridge between complex business requirements and sound technical execution. Your decisions will shape not just what we build today, but how we build for the next several years.

Your Responsibilities

Technical Architecture and System Design:

Lead the architectural design of our data platform, including ETL pipeline topology, data lakehouse layer strategy (raw, curated, and consumption layers), and cross-system integration patterns. Define how data flows from operational sources through BigQuery into SQL Server consumption targets, ensuring designs are built for performance, schema evolution, and long-term maintainability. Evaluate trade-offs across batch and incremental load strategies, partitioning and clustering schemes, and storage formats. Produce architecture decision records (ADRs) and design documents that the broader team can build against.

Data Foundations Ownership:

Own and actively defend the team's data foundations: the modeling standards, schema governance, naming conventions, data quality rules, and documentation practices that make our platform reliable and trustworthy over time. Identify where foundational gaps exist, prioritize closing them, and make the case to leadership when technical debt in the foundation is creating downstream risk. Ensure that every layer of the stack, from staging to consumption, reflects the same discipline and intentionality.

Data Platform Engineering:

Own the engineering of mission-critical data pipelines at scale. Design and implement ETL workflows that move data reliably from source systems into BigQuery, applying dimensional modeling best practices including star schema design and fact table grain definition. Establish partitioning and clustering strategies that optimize BigQuery query performance and control scan costs. Define and enforce data contracts between producers and consumers to reduce coupling and prevent schema drift from breaking downstream systems.

Pipeline Orchestration and Operational Reliability:

Design orchestration patterns using tools such as Airflow or SSIS that are resilient, observable, and easy to operate. Define SLO and SLA frameworks for pipeline execution, establish alerting and retry standards, and lead incident response for platform-level failures. Drive adoption of CI/CD practices for data pipeline deployments, including automated testing, environment promotion, and rollback strategies. Ensure that pipeline failures surface actionable signals rather than silent data loss.

Data Quality, Observability, and Governance:

Define the team's approach to data quality: what gets tested, at what layer, and what constitutes a blocking failure versus a warning. Implement data observability practices including row count validation, schema drift detection, freshness checks, and primary key integrity verification across pipeline stages. Establish column-level lineage documentation so downstream consumers can trace how any metric is derived. Partner with stakeholders to define and enforce data governance policies around access control, row-level and column-level security in BigQuery and SQL Server, and data classification.

Engineering Standards and Code Quality:

Set and maintain the technical bar for the team's engineering output. Author and maintain SQL and ETL pipeline coding standards, review pull requests with a focus on correctness, performance, and long-term maintainability, and establish query optimization practices including execution plan analysis, predicate pushdown, and efficient join strategies in BigQuery. Champion the use of version control, code documentation, and playbooks as first-class engineering artifacts, not afterthoughts.

Technical Mentorship and Team Enablement:

Actively mentor other Data Engineers through code reviews, pairing sessions, and structured technical feedback. Invest specifically in building the team's data foundations skills: SQL quality, data modeling rigor, documentation habits, and operational discipline. Identify skill gaps and propose learning paths or internal knowledge-sharing sessions to close them. Contribute to hiring by designing technical assessments and conducting structured interviews that evaluate foundational depth alongside systems design.

Versatility and Organizational Range:

Step into gaps without being asked. On a lean E2E team, the Staff Engineer is often the person who picks up what falls through the cracks, whether that means jumping into an incident, supporting a partner presentation, reviewing a data model outside your immediate area, or helping onboard a new team member. You bring technical depth but you do not stay in your lane when the team needs something else from you.

Cross-functional Technical Partnership:

Serve as the primary technical point of contact for complex, cross-functional data initiatives. Translate ambiguous business requirements into well-scoped technical proposals, presenting trade-offs and recommendations to both engineering and non-engineering audiences. Influence product and data strategy by proactively identifying opportunities where better data architecture can unlock business capability. Build credibility with partners through technical depth and reliable delivery.

Your Qualifications

• Education: Bachelor's degree in Computer Science, Information Technology, or a related field. Advanced degree a plus.
• Experience: 7 or more years of hands-on Data Engineering experience, with at least 2 years in a technical lead or staff-level role. Demonstrated experience making architectural decisions that others built against in a production environment.
• Data Foundations Mindset: You believe strongly in getting the basics right and you have a track record of building teams and systems that reflect that belief. SQL quality, model integrity, data quality discipline, and documentation are not things you tolerate being done poorly, even when there is deadline pressure to cut corners.
• SQL Mastery: Expert-level SQL across analytical query engines and relational databases (SQL Server, PostgreSQL, or equivalent). You read query execution plans, diagnose performance bottlenecks, and write optimization recommendations the same way others write code reviews. You set the SQL standard for the team.
• Data Modeling Depth: Strong command of dimensional modeling techniques including star and snowflake schema design, fact table grain definition, and conformed dimension management. Experience evaluating physical model trade-offs across OLAP and OLTP targets.
• BigQuery and GCP Platform: Deep hands-on experience with BigQuery, including partitioning and clustering design, cost governance through slot and byte management, column-level and row-level security, authorized views, and scripting with BigQuery procedural SQL. Familiarity with the broader GCP data ecosystem, including Cloud Storage and Dataproc.
• ETL Architecture and Pipeline Design: Proven track record designing and optimizing production ETL pipelines at scale. Deep understanding of full and incremental load strategies, watermark-based change detection, and change data capture (CDC). Experience designing staging layer architecture and applying transformation logic and business rules within the pipeline layer prior to target load. Skilled in building idempotent load patterns that support reliable reruns and recovery.
• Orchestration and Operational Reliability: Extensive experience with workflow orchestration tools such as Apache Airflow or SSIS. Ability to design orchestration topology for complex dependency graphs, define SLO and SLA standards, and build operational runbooks that engineers can actually use.
• Data Quality and Observability: Experience implementing data quality frameworks including automated row count reconciliation, schema drift detection, referential integrity checks, and freshness monitoring. Familiarity with tools such as Great Expectations, dbt tests, or equivalent.
• Programming: Proficiency in at least one programming language commonly used in data engineering workloads (such as Python, Java, Scala, or C#). You write clean, testable, and maintainable code and apply the same quality bar to scripts as to production systems.
• CI/CD for Data Pipelines: Experience integrating data pipeline deployments into CI/CD workflows, including automated testing, environment-specific configuration management, and deployment gating based on data quality checks.
• Versatility and Range: Comfortable contributing across architecture, hands-on engineering, mentorship, and partner-facing work in the same week. You do not define your role narrowly and you do not wait for perfect conditions to step into something new.
• Technical Leadership and Mentorship: Demonstrated ability to raise the technical output of a team through code reviews, standards documentation, architecture guidance, and mentorship.
• Communication and Influence: Able to present architectural proposals and technical trade-offs clearly to both engineering and executive audiences. Skilled at influencing technical direction across teams you don't control and building organizational alignment on complex decisions.

At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.

Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.

For information about benefits and eligibility, see One.Walmart.

The annual salary range for this position is $110,000.00 - $220,000.00. Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include:
* Stock

Minimum Qualifications

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Option 1: Bachelor’s degree in Computer Science and 4 years' experience in software engineering or related field. Option 2: 6 years’ experience in software engineering or related field. Option 3: Master's degree in Computer Science and 2 years' experience in software engineering or related field.

3 years' experience in data engineering, database engineering, business intelligence, or business analytics.

Preferred Qualifications

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Data engineering, database engineering, business intelligence, or business analytics, ETL tools and working with large data sets in the cloud, Master’s degree in Computer Science or related field and 4 years' experience in software engineering or related field. We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture.

Primary Location

802 Respect Dr, Bentonville, AR 72716, United States of America

Walmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.