Description

Data platform that bridges marketing strategy to scaled activation.

Requirements

Must have 10+ years of experience

Experience architecting ML-based solutions in conjunction with DS teams, software engineering teams, and Product teams.

Proven experience translating data science prototypes into production services with clear APIs, SLAs/SLOs, and acceptance criteria in high-volume, low-latency contexts (e.g., AdTech).

Proven experience designing, building, and operating batch/streaming feature pipelines with schema control, validation, lineage, and offline/online parity using Python, Airflow/Composer, Kafka, and BigQuery; leveraging Spark, MySQL, and Redis as appropriate.

Proven experience implementing reproducible ML training workflows (data prep, hyperparameter tuning, evaluation) with artifact and model versioning on public cloud (GCP strongly preferred).

Proven experience packaging and deploying models as containers/services with staged promotion, canary/shadow/A/B rollouts, rollbacks, and environment parity via CI/CD.

Proven experience running scalable inference (batch, microservice, streaming) that meets latency/error budgets, with autoscaling, observability, and SRE-style reliability practices.

Proven experience establishing CI/CD for data and models with automated tests, data quality gates, model regression/drift detection, and API/data contract testing.

Proven experience applying DevSecOps in ML systems: IAM, secrets management, network policies, vulnerability scanning, artifact signing, and policy-as-code on GCP.

Proven experience collaborating with data science on feature design, labeling/annotation strategies, evaluation metrics, error analysis, and defining retraining triggers/schedules.

Exposure to contributing to product strategy and KPI definition; planning experiments (A/B) and prioritizing ML features aligned to SaaS delivery and operational needs.

Exposure to coaching and uplifting teams on data/ML testing, observability, CI/CD, trunk-based development/XP, and writing clear documentation (design docs, runbooks, model/data cards).

Proven experience operating in ambiguous, fast-changing environments; iterating from prototype to production with safe rollouts, clear ownership, and continuous improvement.

Strong English, excellent influencing and communication skills, and excellent documentation skills.

Job responsibilities

Work with product, product engineering, data engineering, and data science peers to build and support our AdTech platform.

Build data-oriented solutions that are simple, scalable, reliable, secure, maintainable, and make a measurable impact.

Provide our teams with the data they need to build, sell, and manage our platform, and scale DS prototypes into production solutions. Develop, deliver and maintain batch and real-time data pipelines, analysis services, workflows and orchestrations, and create and manage the platforms and data infrastructure that hold, secure, cleanse and validate, govern, and manage our data.

Manage our data platform, incorporating services using Airflow, CloudSQL, BigQuery, Kafka, Dataproc, and Redis running on Kubernetes and GCP.

Support our Data Science teams with access to data, performing code reviews, aiding model evaluation and testing, deploying models, and supporting their execution.

Employ modern pragmatic engineering principles, practices, and tooling, including TDD/BDD/ATDD, XP, QA Engineering, Trunk Based Development, Continuous Delivery, automation, DevSecOps, and Site Reliability Engineering.

Contribute to driving ongoing improvements to our engineering principles, practices, and tooling. Provide support & mentorship to junior engineers.

Develop and maintain a contemporary understanding of AdTech developments, industry standards, partner and competitor platform developments, and commercial models, from an engineering perspective.

What we offer

Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.

Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centers or client facilities!

Work-Life Balance: GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.

Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training(GL Vantage, Toast Master),Stress Management program, professional certifications, and technical and soft skill trainings.

Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Group Term Life Insurance, Group Personal Accident Insurance , NPS(National Pension Scheme ), Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.

Fun Perks: We want you to love where you work, which is why we host sports events, cultural activities, offer food on subsidies rates, Corporate parties. Our vibrant offices also include dedicated GL Zones, rooftop decks and GL Club where you can drink coffee or tea with your colleagues over a game of table and offer discounts for popular stores and restaurants!

About GlobalLogic

GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world's largest and most forward-thinking companies. Since 2000, we've been at the forefront of the digital revolution – helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.

Principal Data Engineer IRC276334