Data Engineering Lead

About the role

Deriv is hiring a Data Engineering Lead in Cyberjaya to lead a data engineering team and own its delivery, growth and operating standard. The role sits alongside a Tech Lead who owns technical direction, while the Lead owns the environment in which great engineering happens. It is a management position in a company where data engineering underpins product analytics, compliance, trading systems and AI/ML.

Key responsibilities

• Own delivery velocity as a real metric: track cycle time, automation coverage and governance incident reduction, and set OKRs tied to pipeline reliability, throughput and data quality.
• Remove blockers early (process, tooling, dependencies, ambiguity) and build a team that operates with autonomy, coaching engineers and handling performance gaps directly.
• Own the reliability of data flowing to internal systems, external platforms and AI/ML workloads, including data quality frameworks, lineage tracking, anomaly detection and SLA management.
• Turn data contracts, SLAs and SLOs into things the team builds and monitors, and make pipelines reliable and secure by design.
• Set standards for AI coding assistant usage, measure the productivity shift, and automate scheduling, quality checks, deployment and alerting.
• Translate business needs into an executable roadmap with product, finance, compliance and leadership, and partner with the Tech Lead to align technical direction and priorities.

Required profile

• 10+ years in data engineering, with 3+ years in an engineering leadership role, having managed delivery velocity over multiple quarters.
• Experience leading data engineering teams, not just working in them.
• Experience building governance frameworks and enforcing CI/CD standards for data pipelines (version control, testing, automated deployment).
• Experience embedding AI coding assistants into a team's workflow and measuring outcomes.
• Clear communication of progress, risks and trade-offs to technical and non-technical stakeholders, and ability to coach engineers across levels.

Required skills

• Cloud data stacks: GCP, BigQuery, Airflow (or equivalent)
• Transformation: dbt, Dataform; streaming: Kafka, Pub/Sub; languages: Python, SQL
• Data modelling such as Kimball or Data Vault; pipeline observability tooling
• Good to have: exposure to low-code integration tools such as Fivetran or RudderStack; background in financial services, fintech or regulated environments