Location: Gulberg 3, Lahore
Work Mode: On-site | Full-Time
Experience: 5+ Years

About the Job

We’re looking for a Lead Data Engineer to own and scale the end-to-end data infrastructure that powers marketing, monetization, and analytics across our gaming portfolio.

You’ll design, build, and optimize data pipelines that integrate data from Adjust, ad network APIs, and in-game sources into our data stack. As a team leader, you’ll set the technical direction, establish best practices, and mentor other engineers to build reliable, scalable data systems. You’ll also help shape how the team adopts AI-assisted and agentic development in its daily workflows.

Key Responsibilities

• Lead the architecture, design, and optimization of our data platform (AWS → Iceberg → StarRocks).
• Own the platform’s infrastructure as code. Provision and manage AWS resources (S3, Glue, Lambda, EC2, EBS, ECS, Network Load Balancers, IAM, and networking), along with data platform components, using Terraform and modular, peer-reviewed, version-controlled configurations.
• Deploy and operate self-hosted platform services such as StarRocks, Apache Superset, and Airflow on EC2 and ECS. This includes EBS storage sizing and performance optimization, Network Load Balancer configuration, high availability, scaling, and cost optimization.
• Design and manage GitLab CI/CD pipelines for data pipelines, dbt models, and Terraform code, covering automated testing, schema validation, plan/apply workflows, and controlled promotion across environments.
• Build and maintain ETL/ELT pipelines that ingest data from:
  • Mobile Measurement Partners (MMPs), such as Adjust
  • Ad network APIs (Meta, Google Ads, AppLovin, Unity Ads, IronSource, etc.
  • Monetization and in-app purchase data sources
• Define and enforce data engineering best practices, including testing, code reviews, version control, documentation, and release management.
• Drive the team’s adoption of agentic development by establishing conventions for using AI coding agents (e.g., Claude Code, Cursor, and GitHub Copilot) to scaffold pipelines, generate tests, refactor code, and write documentation, ensuring human review of every change before deployment.
• Collaborate with analysts, data scientists, and marketing teams to design performant data models and enable self-service reporting through Apache Superset.
• Establish a data roadmap focused on scalability, cost efficiency, and reliability.
• Implement data observability, alerting, and performance monitoring.
• Mentor and support junior data engineers while promoting high engineering standards.

Requirements

• 5+ years of experience in data engineering or related roles.
• ​​​​​​​Bachelor’s degree in a STEM field (Computer Science, Engineering, Mathematics, or a related discipline).
• Strong expertise in:
  • AWS (S3, Glue, Lambda, Athena, EC2, EBS, ECS, and Network Load Balancer)​​​​​​
  • Apache Iceberg or similar lakehouse technologies (Delta Lake, Hudi)
  • StarRocks / CelerData, ClickHouse, or similar MPP systems
• Hands-on experience with Terraform or a similar infrastructure-as-code tool (CloudFormation, Pulumi), including managing state, modules, and multiple environments.
• ​​​​​​​Proven experience building and maintaining CI/CD pipelines, ideally using GitLab CI/CD, for both data and infrastructure code.
• Proficiency in Python for data processing and API integration.
• Strong SQL and data modeling skills.
• Hands-on experience integrating marketing and ad network APIs.
• Familiarity with Airflow, dbt, or other orchestration and transformation tools.
• Familiarity with agentic development, including practical experience using AI coding agents in daily workflows and sound judgment about where they can accelerate delivery and where their output requires careful review.
• Strong communication, leadership, and cross-functional collaboration skills.

Nice to Have

• Experience with real-time streaming technologies (Kafka, Flink, Kinesis).
• ​​​​​​​Experience with Apache Superset, including dashboard development, performance tuning, and administration, or with other BI tools (Power BI, Looker, DataLens).
• Experience in gaming analytics, user acquisition, or pLTV modeling.
• Experience setting up shared agentic tooling for a team, such as context and prompt conventions, custom agent workflows, or MCP integrations with internal systems.
• Experience with Docker and Kubernetes-based orchestration (EKS).