Machine Learning Engineer II

The Coca-Cola Company; Atlanta (GA); On-site; USD 180,000 - 240,000; Full time

Job summary

The Coca-Cola Company seeks a Senior Manager, Software Engineer, Data Platform & Segmentation to own the technical vision and architecture of data platforms powering Customer and Commercial product teams. You will be hands-on and act as a multiplier across teams.

You will shape data models and segmentation frameworks, apply Azure data services and ML-enabled segmentation, and drive reliable pipelines, delivery velocity, and measurable business outcomes.

Qualifications

• Extensive experience with Microsoft Azure data services.
• Proven ability to design scalable data models for segmentation.
• Experience leading ML-enabled segmentation pipelines.

Responsibilities

• Partner with product teams to co-own outcomes beyond data assets.
• Translate business questions into durable segmentation models.
• Lead end-to-end technical initiatives in the data platform.
• Mentor senior engineers without management duties.

Skills

Azure Data Platform

ML Engineering

Data Modeling

Segmentation

Product partnership

Tools

Azure Data Factory

Azure ML

Big Data tools

Job description

The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model. This role functions as a hands‑on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes. The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.

Core Accountabilities

• Partner closely with Product Managers, Designers, and Tech Leads to co‑own outcomes, not just data assets.
• Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
• Translate business and customer questions into durable data models and segmentation frameworks.

Product Model & Discovery Partnership

• Partner closely with Product Managers, Designers, and Tech Leads to co‑own outcomes, not just data assets.
• Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
• Translate business and customer questions into durable data models and segmentation frameworks.

Machine Learning

• Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies
• Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
• Collaborating with data scientists
• managing standards for model life cycle, monitoring drift, retraining, etc.
• Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines

Data Platform & Segmentation Architecture

• Define and evolve the segmentation architecture across customer and commercial data domains.
• Design scalable data models that support real‑time, near real time, and batch segmentation use cases.
• Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
• Make explicit trade‑offs across latency, accuracy, cost, privacy, and maintainability.

Engineering Execution & Data Quality

• Build and maintain high‑quality, production‑grade data pipelines and services.
• Ensure strong standards for data quality, lineage, observability, and reliability.
• Reduce fragmentation and duplication in segmentation logic across teams.
• Leverage metrics to continuously improve data freshness, accuracy, and usability.

Individual Contributor Technical Leadership

• Act as a go‑to expert for data platform and segmentation design.
• Lead complex technical initiatives end‑to‑end through hands‑on contribution.
• Influence technical direction through design reviews, reference implementations, and documented standards.
• Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).

Microsoft Azure Data Platform & Fabric Expertise

• Demonstrate deep, hands‑on expertise with Microsoft Azure data services and their application in large‑scale, product‑centric environments.
• Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
• Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
• Optimize designs across cost, performance, latency, and scalability, using Azure‑native capabilities and patterns.
• Ensure secure‑by‑design implementations aligned with Azure identity, access, encryption, and compliance controls.
• Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
• Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.

Business Partnership & Communication

• Serve as a trusted technical partner to Customer and Commercial stakeholders.
• Communicate segmentation concepts, assumptions, and limitations in clear business language.
• Proactively surface data constraints, privacy considerations, and trade‑offs to enable informed decisions.
• Support external partner and vendor conversations as a technical authority when needed.

Governance, Privacy & Compliance

• Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
• Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
• Advocate for responsible and ethical use of customer and commercial data.

Success Measures

• Segmentation capabilities measurably improve customer engagement and commercial outcomes.
• Reduced duplication and inconsistency in segmentation logic across products.
• Improved data quality, freshness, and trustworthiness.
• Faster time‑to‑insight and activation for product teams.
• Platforms and models that scale with growth while controlling cost and risk.

Leadership Profile

• Outcome‑driven, not data for data’s sake
• Deep technical expertise with strong product intuition
• Influences through credibility and clarity, not authority
• Comfortable operating in ambiguity and evolving problem spaces