Customer Engineer: Data

About the role

As a Data Customer Engineer at ADG, you act as a technical authority and trusted advisor at the intersection of enterprise data architecture, strategic consulting and technical pre-sales. You own the technical relationship with prospective and existing clients across the full customer lifecycle, from technical discovery and data architectural whiteboarding to solution validation and commercial closure. The role covers end-to-end pre-sales execution, working with Technical Sales and Delivery teams to guide C-level executives and engineering teams, size and price data solutions, and hand over to post-sales implementation.

Key responsibilities

• Partner with technical sales teams to qualify opportunities, lead technical discovery, run whiteboarding sessions and differentiate ADG's data and analytics capabilities.
• Design and build rapid prototypes, proofs of concept (PoCs), reusable data architectural assets and interactive demonstrations to validate customer requirements.
• Lead technical authoring and architectural strategy for RFPs, RFQs, RFIs and complex tenders, including data infrastructure sizing, bills of materials and deployment roadmaps.
• Formulate long-term technical strategies such as migrating legacy data warehouses to modern cloud data lakes or lakehouses, and uncover new analytics and ML workloads.
• Run architectural reviews, deep-dive workshops and executive briefings with data engineering leads, data architects and C-level stakeholders.
• Coordinate with delivery, migration and professional services teams to package the solution into delivery plans, support pricing and statement of work alignment, and enable post-sales onboarding.

Required profile

• Active cloud data architect or data engineering certification with a major hyperscaler (e.g. Google Cloud Professional Data Engineer, AWS Certified Data Analytics, Microsoft Azure Data Engineer Associate).
• Prior background in a dedicated Data Architect, Data Engineer or deeply technical pre-sales capacity.
• Proven experience presenting complex data architectures to technical stakeholders, business partners and executives.

Required skills

• Data warehousing (BigQuery, Snowflake, Redshift), data lakes, ETL/ELT and streaming data (Spark, Hadoop, Kafka), including cloud migration.
• Hands-on prototyping with Python, SQL, Scala or Bash, infrastructure as code, and building data pipelines, PoCs or dashboards.
• Data security and governance: encryption, identity management, access control, DLP and regulatory compliance.
• Data modeling, query optimization, pipeline debugging and systems design.