Interested in this AI/ML Engineer role at Delta Faucet Company?
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About This Role
At Delta Faucet, we are committed to transforming everyday experiences with water through innovative products and exceptional service. As a leader in the plumbing industry, we take pride in our dedication to quality, sustainability, and customer satisfaction. We believe that diverse perspectives strengthen our mission to create solutions that inspire and elevate the lives of all our customers. We welcome individuals from all backgrounds to join us on this journey toward inclusivity and excellence.
Your Role at Delta Faucet:
Position Summary
The Senior Analytics Engineer owns the layer between curated enterprise data and business consumption. Working primarily in Databricks, Unity Catalog, and Power BI, this role designs and maintains certified data products, semantic models, standardized metrics, and quality controls used by analysts, self-service users, dashboards, and AI agents.
This role replaces a traditional Data Engineer position. Its primary accountability is analytics readiness and trust, rather than source ingestion or ETL throughput.
Why the Role Matters
- Creates a common, governed foundation for reporting, analysis, self-service, and AI.
- Moves repeat logic out of individual reports and into reusable certified assets.
- Reduces conflicting metrics, duplicate datasets, and analyst preparation effort.
- Enables the Decision Intelligence value stream from Intake to Insight to Impact.
Core Responsibilities
- Build and maintain certified facts, conformed dimensions, curated views, and Gold-layer data products.
- Own Power BI semantic models, shared measures, hierarchies, relationships, security, and performance.
- Translate agreed business rules and KPI definitions into transparent, testable, version-controlled logic.
- Implement Unity Catalog governance, metadata, access controls, lineage, certification, and deprecation standards.
- Automate data quality, reconciliation, freshness, and anomaly monitoring for certified products.
- Publish reusable, documented assets that support self-service analytics and governed AI consumption.
Key Partnerships
- Analysts and visualization partners: provide trusted models and absorb recurring logic into governed assets.
- Business owners and data stewards: align definitions, priorities, certification evidence, and acceptable quality.
- IT and data platform teams: define curated input contracts and resolve upstream source issues.
- AI and automation engineers: provide secure, described, retrieval-ready data for agents and workflows.
Required Qualifications
- Bachelor's degree in a quantitative or technology field, or equivalent practical experience.
- 5+ years in analytics engineering, data engineering, BI engineering, or data platform roles, including experience with curated analytics layers or semantic models.
- Advanced SQL and strong dimensional modeling skills, including fact grain, conformed dimensions, and star schemas.
- Production experience with a cloud Lakehouse or warehouse; Databricks and Delta Lake strongly preferred.
- Power BI semantic modeling experience, including DAX, relationships, security, and optimization.
- Experience with data governance, metadata, lineage, quality controls, Git, code review, and deployment discipline.
- Ability to translate business questions and metric definitions into scalable data products and plain-language documentation.
Preferred Experience
- POS, omnichannel retail, consumer products, or manufacturing data.
- Dataset certification, data product management, dbt or Lakeflow/DLT, and Databricks Workflows.
- Preparing governed data for AI agents, RAG, forecasting, or advanced analytics.
Why Join Us?
At Delta Faucet Company, our people are our greatest assets. We value different perspectives and fostering an inclusive environment. You’ll have the opportunity to shape the future of our brand, working alongside passionate professionals committed to excellence and innovation. Join us to lead progressive growth and make a significant impact within a leading organization.
Here are some of the benefits we offer for your personal and professional growth:
- Culture: Recognized and award-winning reputation for equality, diversity and inclusion, flexibility, work-life balance, and more.
- Wellbeing: Comprehensive benefit plans; retirement, savings, tuition reimbursement, and employee incentive programs; resources for mental, physical, and financial wellbeing.
- Learning & Development: LinkedIn Learning access; internal opportunities to work on projects cross-company.
- Social Impact: Four employee-led and self-directed Business Resource Groups; Paid volunteer day annually; Employees share their time, skills and talent with charities and nonprofit organizations across the U.S. and around the globe.
Company: Delta Faucet Company
Full time Hiring Range: $88,700.00 - $139,260.00 USD Actual compensation may vary based on various factors including experience, education, geographic location, and/or skills.Delta Faucet Company (the “Company”) is an equal opportunity employer and we strive to employ the most qualified individuals for every position*.* The Company makes employment decisions only based on merit. It is the Company’s policy to prohibit discrimination in any employment opportunity (including but not limited to recruitment, employment, promotion, salary increases, benefits, termination and all other terms and conditions of employment) based on race, color, sex, sexual orientation, gender, gender identity, gender expression, genetic information, pregnancy, religious creed, national origin, ancestry, age, physical/mental disability, medical condition, marital/domestic partner status, military and veteran status, height, weight or any other such characteristic protected by federal, state or local law. The Company is committed to complying with all applicable laws providing equal employment opportunities. This commitment applies to all people involved in the operations of the Company regardless of where the employee is located and prohibits unlawful discrimination by any employee of the Company.
Delta Faucet Company is an E-Verify employer. E-Verify is an Internet based system operated by the Department of Homeland Security (DHS) in partnership with the Social Security Administration (SSA) that allows participating employers to electronically verify the employment eligibility of their newly hired employees in the United States. Please click on the following links for more information.
*E-Verify Participation Poster:*English & Spanish
E-verify Right to Work Poster:English,**Spanish
Salary Context
This $88K-$139K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Delta Faucet Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($113K) sits 46% below the category median. Disclosed range: $88K to $139K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Delta Faucet Company AI Hiring
Delta Faucet Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US. Compensation range: $139K - $139K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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