Head of Data Engineering

Job Title: Director / Head of Data Engineering OTT Streaming Platform

Location:

Role Overview

Director / Head of Data Engineering

data scalability, accuracy, and security

Key Responsibilities

1. Data Strategy & Architecture

• Define and execute the

  long-term vision

   for enterprise-scale data infrastructure, ensuring it supports business growth, innovation, and monetization strategies.
• Architect and evolve a

  hybrid real-time and batch data platform

   capable of handling petabyte-scale workloads.
• Drive

  AI-first

   design principles in data processing, warehouse optimization, and metadata management.
• Evaluate and implement

  automation-first pipelines

   for hourly, daily, and event-driven data updates.

2. AI & Automation Leadership

• Lead the integration of

  AI/ML-based solutions

   for schema optimization, anomaly detection, metadata enrichment, automated ETL orchestration, and query performance tuning.
• Champion

  generative AI applications

   for automated documentation, pipeline debugging, and data quality reporting.
• Build

  predictive analytics capabilities

   into the platform to anticipate scaling needs, detect streaming anomalies, and optimize ad delivery in real time.
• Deploy

  AI-powered monitoring

   to proactively identify and resolve data issues before they impact business metrics.

3. Team Leadership & Development

• Lead, mentor, and scale a high-performing

  global data engineering team

   including managers, senior engineers, and data architects.
• Foster a

  culture of innovation, automation, and continuous learning

  .
• Collaborate closely with Data Science, Product, Ad Tech, and Engineering leadership to align priorities and deliver measurable outcomes.

4. Data Quality, Governance & Compliance

• Oversee data governance frameworks ensuring

  accuracy, completeness, lineage, and security

  .
• Ensure compliance with

  GDPR, CCPA, SOC 2

  , and other relevant regulations.
• Integrate

  AI-enabled governance tools

   for policy enforcement, data cataloging, and automated classification of sensitive information.

5. Cross-Functional Business Impact

• Partner with content, marketing, ad sales, and operations to ensure that

  data insights directly drive revenue growth, engagement, and retention

  .
• Provide strategic input on

  personalization engines, ad targeting algorithms, and customer lifecycle analytics

  .
• Translate complex technical concepts into actionable business strategies for the executive team.

Qualifications

Required:

• Bachelors/Master’s degree in Computer Science, Data Engineering, or related field; PhD a plus.

• 12+ years

   in data engineering, with at least

  5 years in leadership roles

   managing large-scale data platforms.
• Proven track record in

  OTT, media streaming, or other high-scale consumer technology

   environments.
• Expertise in

  cloud-based data platforms

   (AWS, GCP, Azure) and data warehousing technologies (Snowflake, BigQuery, Redshift, Databricks).
• Deep knowledge of

  distributed data processing frameworks

   (Apache Spark, Flink, Beam).
• Strong experience with

  real-time streaming data systems

   (Kafka, Kinesis, Pub/Sub).
• Hands-on success in

  applying AI/ML to data engineering

  , including automation, performance tuning, metadata enrichment, and anomaly detection.
• Exceptional ability to design and implement

  highly available, cost-optimized, and performance-focused

   data ecosystems.

Preferred:

• Background in

  ad tech and monetization analytics

   for AVOD/FAST platforms.
• Experience integrating

  recommendation systems, A/B testing platforms, and audience segmentation pipelines

  .
• Familiarity with

  MLOps, feature store architectures, and model monitoring

  .

Key Skills & Attributes

• Visionary leadership with

  hands-on technical credibility

  .
• Strategic thinking balanced with execution focus.
• Strong business acumen—able to link data engineering excellence with revenue, retention, and cost optimization.
• Passion for

  AI-driven transformation

   in data engineering.
• Excellent communication and storytelling skills for C-suite and technical audiences alike.

Performance Metrics

• Reduction in

  data pipeline latency

   and operational overhead through automation.
• Measurable

  AI-driven efficiency gains

   in processing, storage, and analytics.
• Data platform uptime, cost optimization, and scalability benchmarks.
• Quality and timeliness of business insights delivered.
• Team engagement, retention, and innovation output.