Engineering Manager -MLOps at dunnhumby

Gurugram, Haryana, India

Apply Now

Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Retail, Retail Media, Customer Data ScienceIndustries

Requirements

Proven experience in microservices architecture, with hands-on knowledge of Docker and Kubernetes for orchestration
Proficiency in ML Ops and Machine Learning workflows using tools like Spark
Strong command of SQL and PySpark programming
Expertise in Big Data solutions such as Spark and Hive, with advanced Spark optimizations and tuning skills
Hands-on experience with Big Data orchestrators like Airflow
Proficiency in Python programming, particularly with frameworks like FastAPI or equivalent API development tools
Experience in data engineering (added advantage)
Experience in unit testing, code quality assurance, and the use of Git or other version control systems
Practical knowledge of cloud-based data stores, such as Redshift and BigQuery (preferred)
Experience in cloud solution architecture, especially with GCP and Azure
Familiarity with GitLab CI/CD pipelines (bonus)
Solid understanding of logging, monitoring, and alerting systems for production-level big data pipelines
Prior experience with scalable architectures and distributed processing frameworks
A collaborative approach to working within cross-functional teams
Ability to troubleshoot complex systems and provide innovative solutions
Familiarity with GitLab for CI/CD and infrastructure automation tools (added advantage)

Responsibilities

Lead a team of Big Data and MLOps engineers
Mentor and develop a high-performing team
Drive best practices in software development, data management, and model deployment
Ensure robust technical design for secure, scalable, and efficient solutions
Perform hands-on development to tackle complex challenges
Collaborate across teams to define requirements
Deliver innovative solutions
Keep stakeholders and senior management informed on progress, risks, and opportunities
Stay ahead of advancements in AI/ML technologies and drive their application
Adopt an agile mindset to overcome challenges and deliver impactful solutions
Ensure operational efficiency and deliver high-value solutions
Contribute to system architecture
Ensure adherence to engineering best practices

Skills

Key technologies and capabilities for this role

MLOpsBig DataSoftware DevelopmentData ManagementModel DeploymentSystem ArchitectureScalable DesignSecure SystemsEngineering Best PracticesTeam LeadershipMentoring

Questions & Answers

Common questions about this position

What technical skills are required for the Engineering Manager - MLOps role?

Required skills include proven experience in microservices architecture with Docker and Kubernetes, proficiency in ML Ops and Machine Learning workflows using Spark, strong command of SQL and PySpark, expertise in Big Data solutions like Spark and Hive, hands-on experience with Airflow, proficiency in Python with FastAPI or equivalent, and experience in unit testing, code quality, and Git.

What leadership responsibilities does the Engineering Manager handle?

The role involves leading a team of Big Data and MLOps engineers, mentoring and developing the team, contributing to system architecture, ensuring engineering best practices, and collaborating across teams while keeping stakeholders informed.

Is this Engineering Manager position remote or office-based?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What makes a strong candidate for the Engineering Manager - MLOps position?

A strong candidate will have technical expertise in MLOps, Big Data tools like Spark and PySpark, cloud infrastructure, combined with proven leadership skills, hands-on development experience, and an agile mindset to mentor teams and deliver scalable solutions.

dunnhumby

Customer data analytics for retail optimization

About dunnhumby

dunnhumby specializes in Customer Data Science, focusing on enhancing customer experiences for retailers and brands through data analysis. The company uses advanced analytics to interpret customer behavior, preferences, and trends, which allows clients to implement targeted marketing campaigns. Instead of storing personal data, dunnhumby analyzes data using unique identifiers from browsers and devices to maintain privacy. Its services include media solutions, customer insights, and personalized marketing strategies, which help clients improve customer engagement and sales. Additionally, dunnhumby Ventures invests in early-stage retail technology startups, ensuring the company remains at the forefront of retail innovation. The main goal of dunnhumby is to empower businesses to create better customer experiences through data-driven insights.

London, United KingdomHeadquarters

1989Year Founded

BUYOUT_LBOCompany Stage

Data & Analytics, Consulting, Venture Capital, Consumer GoodsIndustries

1,001-5,000Employees

Benefits

Flexible Work Hours

Unlimited Paid Time Off

Remote Work Options

Risks

Departure of key media team member may disrupt dunnhumby's media strategy.

Challenges in integrating startups with enterprises could misalign innovation goals.

Data privacy concerns may arise from partnerships, affecting client trust.

Differentiation

dunnhumby leverages AI to optimize product selection and inventory management.

The company offers a unique Competitive Threat Evaluator for strategic market insights.

dunnhumby partners with startups through its Retail Innovation Network to drive retail tech.

Upsides

Real-time data analytics partnerships enhance dunnhumby's market adaptability.

AI-powered tools position dunnhumby as a leader in competitive retail analysis.

The Retail Innovation Network fosters collaboration, boosting innovation in retail technology.

Land your dream remote job 3x faster with AI

Try Jobo Free