Verified Job
IT / Software / Data Analyst
Data Engineer
IT / Software / Data Analyst
#14857
Remote / WFH
McDonalds in India
Job Description
Position Summary:
We are seeking a Data Engineer II preferably Full Stack Engineer, to focus on core data engineering work within our RealTime Customer Data Platform (CDP). This role emphasizes building, testing, and operating data pipelines while developing strong fundamentals in streaming and distributed data systems.
Primary Responsibilities:
RealTime CDP & Streaming
Audience Building & Segmentation
Customer Data Schemas & Quality
Leadership & Delivery
Cross Functional Collaboration
Work location: Hyderabad, India
Work pattern: Full time role.
Work mode: Hybrid.
Who Were Looking For:
A hands-on, full stack Data Engineering leader who can build real time and batch customer data pipelines and build audience/segmentation capabilities, scale them globally, and develop worldclass engineering teams that deliver privacy safe, high-performance activation.
Build and scale real time pipelines for clickstream, transactional, and behavioral data using Kafka, Flink, Spark Structured Streaming, or Dataflow/Beam.
Design and evolve customer event models, session-ization, and cross channel stitching to maintain a unified, channel stitching to maintain a unified, privacy aware customer view.
Implement low latency activation APIs used by apps, web, CRM, loyalty, kiosks, and marketing orchestration platforms.
Build and maintain observability, SLAs/SLOs, schema evolution, lineage, and cost efficiency across streaming and batch paths.
Build dynamic audience services for behavioral and lifecycle cohorts, rules driven propensity groupings, and event triggered real time segments.
Define data contracts and versioning for attributes, traits, and segment definitions to ensure reuse, durability, and safe change management.
Setup and configure audience governance rules (freshness SLAs, recency/frequency windows, cardinality limits, consent gates) and ensure theyre consistently enforced.
Create and maintain audience playbooks (e.g., reactivation, onboarding, churn risk, high value, cart abandon).
Deepen market relationships to better understand segmentation and activation needs.
Ideate and propose new capabilities for testing and market validation.
Create customer data schemas (profiles, attributes, segments, preferences, consent) backed by clear SLOs and documentation.
Implement comprehensive data quality, validation, and lineage across all audience and profile pipelines.
Create reference patterns and templates so global markets and channels can integrate quickly and safely.
Enhance collaboration with other product teams to proactively drive and align requirements.
Take active role in leading design discussions, especially around CDP capabilities.
Improve oversight of vendor resources to ensure timely and quality delivery.
Ideate capabilities and roadmaps, manage dependencies, and deliver against business outcomes with clear KPIs and executive reporting.
Build engineering excellence: testing, automation, code quality, observability, and operational readiness.
Collaborate with Product, Mar Tech, Loyalty, Architecture, Data Governance, Security, Legal, and Compliance to align roadmaps and ensure privacy-by-design and security-by-default.
We are seeking a Data Engineer II preferably Full Stack Engineer, to focus on core data engineering work within our RealTime Customer Data Platform (CDP). This role emphasizes building, testing, and operating data pipelines while developing strong fundamentals in streaming and distributed data systems.
Primary Responsibilities:
RealTime CDP & Streaming
Audience Building & Segmentation
Customer Data Schemas & Quality
Leadership & Delivery
Cross Functional Collaboration
Work location: Hyderabad, India
Work pattern: Full time role.
Work mode: Hybrid.
Who Were Looking For:
A hands-on, full stack Data Engineering leader who can build real time and batch customer data pipelines and build audience/segmentation capabilities, scale them globally, and develop worldclass engineering teams that deliver privacy safe, high-performance activation.
Build and scale real time pipelines for clickstream, transactional, and behavioral data using Kafka, Flink, Spark Structured Streaming, or Dataflow/Beam.
Design and evolve customer event models, session-ization, and cross channel stitching to maintain a unified, channel stitching to maintain a unified, privacy aware customer view.
Implement low latency activation APIs used by apps, web, CRM, loyalty, kiosks, and marketing orchestration platforms.
Build and maintain observability, SLAs/SLOs, schema evolution, lineage, and cost efficiency across streaming and batch paths.
Build dynamic audience services for behavioral and lifecycle cohorts, rules driven propensity groupings, and event triggered real time segments.
Define data contracts and versioning for attributes, traits, and segment definitions to ensure reuse, durability, and safe change management.
Setup and configure audience governance rules (freshness SLAs, recency/frequency windows, cardinality limits, consent gates) and ensure theyre consistently enforced.
Create and maintain audience playbooks (e.g., reactivation, onboarding, churn risk, high value, cart abandon).
Deepen market relationships to better understand segmentation and activation needs.
Ideate and propose new capabilities for testing and market validation.
Create customer data schemas (profiles, attributes, segments, preferences, consent) backed by clear SLOs and documentation.
Implement comprehensive data quality, validation, and lineage across all audience and profile pipelines.
Create reference patterns and templates so global markets and channels can integrate quickly and safely.
Enhance collaboration with other product teams to proactively drive and align requirements.
Take active role in leading design discussions, especially around CDP capabilities.
Improve oversight of vendor resources to ensure timely and quality delivery.
Ideate capabilities and roadmaps, manage dependencies, and deliver against business outcomes with clear KPIs and executive reporting.
Build engineering excellence: testing, automation, code quality, observability, and operational readiness.
Collaborate with Product, Mar Tech, Loyalty, Architecture, Data Governance, Security, Legal, and Compliance to align roadmaps and ensure privacy-by-design and security-by-default.
