Manager, Data Engineering [T500-28146]

McDonald's Global Office in India; Hyderabad; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

McDonald's Global Office in India seeks a hands-on Data Engineering leader to drive real-time customer data platform capabilities, architecting streaming pipelines, stateful processing, and robust data models that scale globally.

You will partner with product, marketing, and security teams to deliver low-latency activation APIs, observability, and governance, while mentoring engineers and delivering on ambitious CDP objectives.

Qualifications

• Must have extensive hands-on experience building streaming data pipelines.
• Proven ability to design scalable data models and stateful processing.
• Strong command of SQL and big data tooling in cloud environments.

Responsibilities

• Architect, build, and operate high-throughput streaming pipelines for diverse data sources.
• Design and evolve customer event models and audience features at scale.
• Ensure data quality, observability, and production readiness with SRE partnership.
• Lead delivery, mentor engineers, and drive architectural decisions.

Skills

Data Engineering

Streaming

Kafka

Flink

Spark Structured Streaming

Dataflow/Beam

SQL

GCP

Observability

SRE collaboration

Tools

BigQuery

Athena

Databricks

Confluent

Job description

One of the world’s largest employers with locations in more than 100 countries, McDonald’s Corporation has corporate opportunities in Hyderabad. Our global offices serve as dynamic innovation and operations hubs, designed to expand McDonald’s global talent base and in-house expertise. Our new office in Hyderabad will bring together knowledge across business, technology, analytics, and AI, accelerating our ability to deliver impactful solutions for the business and our customers across the globe.

Position Summary:

We are seeking a Manager, Data Engineering to lead hands-on technical delivery for key components of our RealTime Customer Data Platform (CDP). This role is deeply focused on core data engineering: building scalable streaming pipelines, managing stateful processing, ensuring data quality and reliability, and operating production-grade data systems.

Primary Responsibilities:

RealTime CDP & Streaming:

• Architect, build, and operate high-throughput, low-latency streaming pipelines for clickstream, transactional, and behavioral data using Kafka, Flink, Spark Structured Streaming, or Dataflow/Beam.
• Design stateful stream processing for sectorization, windowing, deduplication, enrichment, and late-arriving data handling.

Core Data Engineering:

• Design and implement robust data models for events, customer attributes, and derived features.
• Manage schema evolution, backward compatibility, and data contracts. Build scalable batch pipelines to support backfills, reprocessing, and historical precomputation aligned with real-time logic.
• Data Quality, Reliability & Operations
• RealTime CDP & Streaming
• Audience Building & Segmentation
• Customer Data Products & Quality
• Leadership & Delivery
• Cross Functional Collaboration

Work pattern: Full time role.

Who We’re Looking For:

A hands-on, full stack Data Engineering leader who can elevate real time customer data and audience/segmentation capabilities, scale them globally, and develop worldclass engineering teams that deliver privacy safe, high-performance activation.

• Implement data quality checks (completeness, validity, timeliness), pipeline-level SLAs/SLOs, and end-to-end observability.
• Own production readiness including monitoring, alerting, capacity planning, and on-call support with SRE partners.
• Architect 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-inaction, 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.
• Implement Eventing frameworks like managed Kafka or Confluent
• Establish standards for observability, SLAs/SLOs, schema evolution, lineage, and cost efficiency across streaming and batch paths.
• Engineer data pipelines and services that power audience segmentation, attribute computation, and activation feeds.
• Ensure efficient data delivery to downstream systems (e.g., CDPs, marketing platforms, APIs) with strict latency and freshness guarantees.
• 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.
• Set and abide by audience governance standards (freshness SLAs, recency/frequency windows, cardinality limits, consent gates) and ensure they’re consistently enforced.
• Partner with Mar Tech and Product to templatize audience playbooks (e.g., reactivation, onboarding, churn risk, high value, cart abandon).
• 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.
• Drive CDP capabilities and roadmaps, manage dependencies, and deliver against business outcomes with clear KPIs and executive reporting.
• Build and maintain engineering excellence: testing, automation, code quality, observability, and operational readiness.
• Lead technical execution, review designs and code, mentor engineers on data engineering best practices, and drive disciplined delivery through sprint planning, estimation, and operational follow-through.
• 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.
• Translate marketing and product activation needs into reusable audience capabilities and APIs.
• SQL Very Strong proficiency in native SQL, has used Big Query or Athena Advanced performance tuning on large datasets.
• Streaming & Processing: Kafka, Flink, Spark/PySpark, Dataflow/Beam.
• Audience/Segmentation: Handson experience building audience engines, cohort generation logic, and audience APIs for activation.
• Data Platforms: GCP, Databricks; Big Data ecosystems (Hadoop, lake house patterns); NoSQL; columnar formats (Parquet).
• Cloud: GCP preferred (Pub/Sub, Big Query, Dataflow, Cloud Run); AWS/Azure acceptable.
• Governance & Reliability: Observability, SLAs/SLOs, validation, consent/privacy controls.
• 7-10 years in largescale Data Engineering / Distributed Systems.
• 5+ years with GCP or AWS (GCP preferred).
• 3+ years working on real time customer data and/or segmentation platforms.
• Experience with CDPs (mParticle, Adobe RTCDP, Braze, Tealium).
• Designing real-time audience builders, rule engines, and activation frameworks.
• Multiregional deployments, data residency, and consent management at global scale.
• Strong stakeholder communication; ability to simplify technical concepts for marketers and product leaders.
• Systems thinker with strong architectural judgment and influence.