About Photon
Photon, a global leader in AI and digital solutions, helps clients accelerate AI adoption and embrace Digital Hyper-expansion to make tomorrow happen today . We work with 40% of the Fortune 100, enabling them to stay agile and future-ready in an era of converging digital and AI boundaries. Powering billions of touchpoints a day, Photon combines AI management, digital innovation, product design thinking, and engineering excellence to drive lasting transformation for F500 clients. We employ several thousand people across dozens of countries. Learn more at
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About the Role
As part of the Mail Analytics Data Engineering team, you will be working on large-scale batch pipelines, data serving, data lakehouse, and analytics systems, enabling mission critical decision making, downstream, AI-powered capabilities, and more.
If you're passionate about building data infrastructure and platforms that power modern Data- and AI-driven business at scale, we want to hear from you!
Your Day
- Partner with Data Science, Product, and Engineering to collect requirements to define the data ontology for Mail Data & Analytics
- Lead and mentor junior Data Engineers to supportMail s ever-evolving data needs
- Design, build, andmaintainefficient and reliable batch data pipelines to populate core data sets
- Develop scalable frameworks and tooling to automate analytics workflows and streamlineusersinteractions with data products
- Establish and promote standard methodologies for data operations and lifecycle management
- Develop new or improve andmaintainexisting large-scale data infrastructures and systems for data processing or serving, optimizing complex code through advanced algorithmic concepts and in-depth understanding of underlying data system stacks
- Create and contribute to frameworks that improve the efficacy of the management and deployment of data platforms and systems, while working with data infrastructure to triage and resolve issues
- Prototype new metrics or data systems
- Define and manage Service Level Agreements for all data sets inallocatedareas of ownership
- Develop complex queries,very largevolume data pipelines, and analytics applications to solve analytics and data engineering problems
- Collaborate with engineers, data scientists, and product managers to understand business problems, technical requirements to deliver data solutions
- Engineering consulting on large and complex datalakehousedata
You Must Have
- BS in Computer Science/Engineering, relevant technical field, or equivalent practical experience, with specialization in Data Engineering
- 8+ years of experience building scalable ETL pipelines on industry standard ETL orchestration tools (Airflow, Composer, Oozie) with deepexpertisein SQL, PySpark, or scala.
- 3+ years leading data engineering development directly with business or data science partners
- Built, scaled, and maintainedMulti-Terabytedata sets and having an expansive toolbox for debugging and unblocking large scale analytics challenges (skew mitigation, sampling strategies, accumulation patterns, data sketches, etc.)
- Experience with at least one major cloud's suite of offerings (AWS, Google Cloud Platform, Azure).
- Developed or enhanced ETL orchestrations tools or frameworks
- Worked within standardGitOpsworkflow (branch and merge, PRs, CI / CD systems)
- Experience working with GDPR
- Self-driven, challenge-loving, detail oriented, teamwork spirit, excellent communication skills, ability to multitask and manage expectations
Preferred
- MS/PhD in Computer Science/Engineering or relevant technical field, with specialization in Data Engineering
- 3+years experiencein Google Cloud Platform technologies (BiqQuery, Dataproc, Dataflow, Composer, Looker)
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