Data Engineer

Role Overview

This is a presales advisory role, not a delivery role, where the employee will partner with account teams across the full sales cycle, bringing technical credibility and business clarity to every customer conversation. The role demands a high degree of autonomy, and the candidate will independently manage tasks and responsibilities while maintaining close virtual collaboration with a team based primarily in the US.

What You Will Do

The employee will help the team scale globally and may be called on from time to time to support opportunities outside the region. The primary focus is the region, but the role supports global AI and data solutions work from a Singapore base.

Why It Might Be a Fit

The ideal candidate is a data scientist who has had to prepare and manage their own data, or a data engineer who has worked closely with data scientists and understands their needs. The role requires a genuine curiosity and a habit of staying current, with the technologies at the center of this role being new, and the right candidate has learned them by using them and is actively exploring how AI is changing the way data work gets done.

Requirements

• Bachelor’s degree in computer science, data engineering, data science, statistics, or a related field, or equivalent experience.
• A minimum of 3 years of relevant technical experience across data engineering and data science, including hands on experience preparing and managing data for data science use cases.
• Customer facing sales experience is required, gained in a presales, consulting, solutions engineering, reseller, or systems integrator setting.
• Hands on experience with Snowflake and/or Databricks is highly desirable, as most customers we meet use one or both.
• Exposure to Microsoft Fabric and similar modern data platforms is also relevant.
• Data engineering fundamentals, including data ingestion and integration, ETL and ELT pipeline design, orchestration, batch and streaming processing, SQL, Python, and Apache Spark, data modeling, data quality and observability, and metadata and lineage.
• Working data science experience, including feature engineering, model development and evaluation, and experimentation.
• Practical understanding of how AI workloads, including large language models (LLMs), retrieval augmented generation (RAG), and agentic AI, consume enterprise data.
• Cloud familiarity from a data and AI perspective, including the ability to move and use data from AWS, Azure, Google Cloud, or Alibaba Cloud environments in AI initiatives.
• Working knowledge of enterprise data governance, security, and data strategy, and how trusted data enables successful AI initiatives.
• Strong communication skills, with the ability to speak with data engineers, data scientists, architects, developers, IT leadership, and executive stakeholders, tailoring technical depth appropriately while maintaining credibility with every audience.
• Advisory mindset and the ability to lead customers through ambiguous technical challenges, structure discovery conversations, identify gaps, evaluate platform tradeoffs objectively, and deliver actionable recommendations.
• Sales experience and commercial awareness to support account teams effectively in fast moving sales cycles, including working as a guest in accounts owned by others.
• Genuine curiosity and a habit of staying current, with the technologies at the center of this role being new, and the right candidate has learned them by using them and is actively exploring how AI is changing the way data work gets done.

Benefits

• Health and Wellbeing: Heath, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
• Financial Benefits: Competitive pay, Profit Sharing, PF Plan with Company Matching, NPS (National Pension Scheme), Life and Disability Insurance, Tuition Reimbursement
• Paid Time Off: PTO & Holidays, Parental Leave, Sick Leave, Day of Caring for social responsibility, Bereavement