[Hiring] Presales Engineers - Data @Prodapt

Role Description

Support the Presales Lead in crafting winning solutions for large-scale data modernisation and AI-ready platform transformation deals. This is a hands-on technical presales role that combines deep data engineering skills across Databricks, Snowflake, and Google Cloud with a strong understanding of AI/ML to build compelling solution architectures, proof-of-concepts, and deal collateral.

Responsibilities

• Solution Engineering & Deal Support
  • Build detailed solution architectures and technical proposals in support of RFPs, proactive pursuits, and strategic deals.
  • Develop effort estimations, platform sizing, and cost models for data modernisation engagements.
  • Create high-quality solution decks, architecture diagrams, and technical write-ups under tight deal timelines.
  • Support the Presales Lead in client-facing workshops, demos, and architecture walkthroughs.
• Data Modernisation for AI
  • Design migration and re-platforming approaches from legacy systems (Oracle, Teradata, Netezza, traditional ETL) to modern platforms.
  • Build reference architectures for Data Warehouse → Lakehouse → AI-ready platform transformations.
  • Develop code conversion strategies and automated migration frameworks.
  • Ensure modernised data platforms are optimised for downstream AI/ML workloads, including feature engineering, model training pipelines, and serving layers.
• Platform Engineering – Databricks, Snowflake & Google Cloud
  • Databricks: Lakehouse architecture, Delta Lake, Unity Catalog, MLflow, Databricks Workflows, and Spark-based processing.
  • Snowflake: Data sharing, Snowpark, Streams & Tasks, dynamic tables, Snowflake Cortex, and governance features.
  • Google Cloud Data & Analytics: BigQuery, Dataproc, Dataflow, Pub/Sub, Dataplex, Vertex AI integration, and Cloud Composer.
  • Build reusable accelerators, templates, and demo environments across these platforms.
• AI & Machine Learning Integration
  • Strong working knowledge of AI/ML concepts, including supervised/unsupervised learning, LLMs, RAG architectures, and GenAI application patterns.
  • Design data pipelines and platform architectures that enable AI readiness — clean, governed, feature-rich, and accessible data.
  • Demonstrate understanding of MLOps practices including model versioning, experiment tracking, and deployment pipelines.
  • Support integration of AI capabilities into solution proposals, such as AI-assisted data quality, intelligent document processing, and predictive analytics use cases.
• Data Governance & Quality
  • Design governance layers within modern platforms (Unity Catalog, Snowflake governance, GCP Dataplex).
  • Define data quality frameworks, lineage tracking, and cataloguing strategies as part of solution designs.
  • Incorporate privacy, compliance, and security best practices into architectures.
• Accelerator Development & Thought Leadership
  • Build and maintain reusable presales assets: reference architectures, estimation templates, demo scripts, and proof-of-concept kits.
  • Contribute to POVs, whitepapers, and technical blogs on data modernisation and AI topics.
  • Stay current with platform releases, industry trends, and competitive landscape across Databricks, Snowflake, and Google Cloud.
• Collaboration
  • Work closely with the Americas Presales Lead to ensure alignment on deal strategy and timelines.
  • Coordinate with delivery teams to validate solution feasibility and transition smoothly from presales to execution.
  • Engage with Databricks, Snowflake, and Google Cloud partner teams for joint solutioning and co-selling activities.

Qualifications

• 8–15 years of experience in data engineering, data platforms, or analytics, with at least 2 years in a presales or solutioning capacity.
• Hands-on expertise in at least two of the three core platforms: Databricks, Snowflake, and Google Cloud Data & Analytics.
• Proven experience in data warehouse modernisation — migration from legacy platforms (Oracle, Teradata, Netezza) to cloud-native architectures.
• Strong knowledge of AI/ML fundamentals, including GenAI, LLMs, RAG, and MLOps. Ability to articulate how data platforms enable AI outcomes is essential.
• Proficiency in SQL, Python, and Spark. Familiarity with infrastructure-as-code (Terraform) is a plus.
• Experience creating solution architectures, technical proposals, and effort estimations for large deals.
• Excellent communication skills — ability to articulate technical solutions to both technical and business audiences.

Preferred Qualifications

• Certifications in Databricks (Data Engineer Associate/Professional), Snowflake (SnowPro Core/Advanced), or Google Cloud (Professional Data Engineer / ML Engineer).
• Experience working with Americas or global clients across time zones.
• Exposure to FinOps and cloud cost optimisation strategies.
• Background in telecom, BFSI, or enterprise verticals.
• Familiarity with data mesh and domain-driven data product architectures.