AI Engineer- Data & Reporting

Role Overview

As an AI Engineer in our Data & Reporting team, you will design and scale next-generation

AI-powered data pipelines that process billions of records and translate them into

meaningful insights. From hourly log processing to automated dashboards to customer

lifetime value (CLV) modelling, you will be responsible for architecting the intelligence layer

that fuels our business and content strategy.

Your work will directly impact executive decision-making, advertiser ROI, and the

streaming experience of millions of users worldwide.

Key Responsibilities

• Architect and scale AI-driven data and reporting pipelines for user and ad log

processing for ingesting structured, semi-structured, and unstructured data from

multiple sources.

• Design automation for hourly ingestion, ETL/ELT workflows to ensure clean,

validated, and enriched data for downstream AI/ML models and reporting and

visualization of large-scale streaming data.

• Develop systems for data extraction and offline reporting automation to reduce

manual dependencies.

• Build pipelines for CLV computation, audience segmentation, and predictive

insights.

• Contribute to data warehouse creation and optimization at petabyte scale.

• Partner with product, marketing, content, and engineering teams to ensure

dashboards and reports power real-time decisions.

• Implement quality checks, anomaly detection, and self-healing workflows for

reliable insights delivery.

What Youll Bring

• Strong foundation in AI/ML, data engineering, and automation.

• Expertise in Python, Scala, or Java for building scalable data pipelines.

• Hands-on experience with big data frameworks (Spark, Flink, Kafka, Beam).

• Proficiency in SQL, data modeling, and warehouse design (Snowflake, BigQuery,

Redshift).

• Familiarity with real-time log processing and stream analytics.

• Knowledge of dashboarding/reporting tools (Tableau, Looker, Power BI) and

automation APIs.

• Experience with cloud ecosystems (AWS, GCP, or Azure) and containerized

deployments (Docker, Kubernetes).

• Bonus: background in ad-tech data, CLV modeling, or predictive analytics.Role & responsibilities