Data Engineering Lead

About the Role
We are seeking a Data Engineering Lead to manage our data platform and lead the engineers who develop it. This platform, operating on GCP (BigQuery, dbt, Airflow, ClickHouse), supports internal reporting, merchant analytics, and AI tooling. As a player-coach, you will remain hands-on with data pipelines, code reviews, and production fixes while guiding the team. This position is based in Dubai, India, or Sri Lanka and offers remote or hybrid work options, with regular overlap with Gulf Standard Time (UTC+4).

What You’ll Own
Technical Leadership

• Lead the team’s day-to-day delivery by planning and prioritizing work, holding regular 1:1s, and removing blockers.
• Define and uphold engineering standards for code review, testing, naming conventions, data contracts, documentation, and on-call responses.
• Mentor engineers through pairing, design reviews, and providing timely, constructive feedback.

Architecture and Platform Direction

• Make and document architectural decisions regarding ingestion, transformation, serving, and orchestration.
• Balance the development of new features with reliability, scalability, and technical debt, ensuring the platform roadmap remains realistic.
• Manage the GCP environment related to the data warehouse, including Cloud Storage, Cloud Run, IAM, and integrations with external cloud-hosted sources.

Data Pipelines and Modelling

• Own the ingestion process from transactional databases, SaaS platforms, application events, and third-party APIs, including CDC, backfills, schema evolution, and handling late-arriving data.
• Oversee the dbt project end to end: staging, intermediate and mart layers, incremental models, tests, documentation, and modeling conventions.
• Optimize BigQuery for performance and cost through partitioning, clustering, and query tuning, with expenditures tracked against a clear budget.
• Design low-latency analytical serving in ClickHouse or a similar OLAP store, focusing on table design, sorting keys, materialized views, and aggregation strategies.

Reliability and Data Quality

• Operate production Airflow workloads with effective scheduling, dependency management, retries, and alerting.
• Establish freshness, completeness, and quality checks, addressing root causes of issues rather than relying on repeated manual fixes.
• Lead incident response and post-incident reviews, converting findings into enduring improvements.
• Develop CI/CD and automated testing for data code to ensure safe deployment of changes to production.

Stakeholders and AI

• Collaborate with product, analytics, finance, and operations teams to translate business needs into a prioritized delivery plan, while challenging unclear value requests.
• Work with security and compliance teams regarding access control, PII handling, and data governance.
• Identify practical applications of LLMs and AI agents for data discovery, quality monitoring, and troubleshooting, advancing successful applications to production.

Who You Are

• You take ownership of outcomes for yourself and your team and make informed decisions with limited information.
• You simplify complexity, understand the costs of decisions, and articulate trade-offs clearly.
• You leverage AI to enhance efficiency without compromising security, quality, or judgment.
• You are committed to developing others and can communicate effectively across teams and time zones.

What You’ll Need

• 6+ years of experience in data or backend engineering, primarily in production systems.
• At least 1 year in a leadership position, mentoring data engineers as a tech lead, team lead, or senior engineer with clear technical ownership.
• Proven experience managing a data platform or significant components of one, focusing on architecture, reliability, and cost.
• Strong Python skills with extensive experience in building and debugging production pipelines and services.
• Advanced SQL proficiency, including window functions, complex transformations, and query performance analysis.
• 2+ years of production experience with dbt: implementing incremental models, macros, tests, and documentation.
• Hands-on experience managing Apache Airflow in production, including backfills, monitoring, and troubleshooting.
• Production experience with BigQuery: data modeling, partitioning, clustering, and optimizations for queries and costs.
• Familiarity with GCP, including Cloud Storage, IAM, and cloud data infrastructure.
• Solid knowledge of PostgreSQL: indexing, query performance, and incremental extraction techniques.
• Experience with Apache Flink or an equivalent stream/distributed processing framework.
• Proficient with Linux: Bash, cron, Docker on VMs, log analysis, SSH, and basic networking.
• Strong engineering practices such as Git, CI/CD, automated testing, and code review.

Nice to Have

• Production experience with ClickHouse, including performance tuning, materialized views, and clustering operations.
• Managed ELT platforms like Airbyte or Fivetran.
• Experience with CDC and event streaming using Datastream, Kafka, or Debezium.
• Familiarity with Kubernetes and Terraform.
• Experience with BI tools such as Tableau or Power BI and a grasp of BI performance metrics.
• Knowledge of LLM APIs, retrieval-augmented generation (RAG), and agent frameworks.
• Experience with data observability tools like Soda, Great Expectations, or Monte Carlo.
• Familiarity with AWS services such as EC2, S3, and RDS.
• Understanding of data governance for payments data, such as PCI DSS.
• Experience in payments, fintech, hospitality technology, or high-growth marketplaces.

What We Offer

• Real ownership over products impacting millions of diners and thousands of restaurants worldwide.
• The chance to tackle challenging problems in payments, hospitality, and customer experience.
• A fast-moving environment where great ideas can quickly be implemented.
• Close collaboration with experienced operators, builders, and industry professionals.
• A unique opportunity to make a significant impact on one of the fastest-growing restaurant technology companies globally.