Presales Engineers - Data

Overview<br><br><p style="margin: 0px;">Presales Data Engineer – Data Modernisation & AI</p><p style="margin: 0px;">Location</p><p style="margin: 0px;">India (Bangalore / Hyderabad / Chennai / Pune — Flexible)</p><p style="margin: 0px;">Role Purpose</p><p style="margin: 0px;">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.</p> <br>Responsibilities<br><br><p style="margin: 0px;">Key Responsibilities</p><ol><li>Solution Engineering & Deal Support</li></ol><p style="margin: 0px;">Build detailed solution architectures and technical proposals in support of RFPs, proactive pursuits, and strategic deals.</p><p style="margin: 0px;">Develop effort estimations, platform sizing, and cost models for data modernisation engagements.</p><p style="margin: 0px;">Create high-quality solution decks, architecture diagrams, and technical write-ups under tight deal timelines.</p><p style="margin: 0px;">Support the Presales Lead in client-facing workshops, demos, and architecture walkthroughs.</p><ol start="2"><li>Data Modernisation for AI</li></ol><p style="margin: 0px;">Design migration and re-platforming approaches from legacy systems (Oracle, Teradata, Netezza, traditional ETL) to modern platforms.</p><p style="margin: 0px;">Build reference architectures for Data Warehouse → Lakehouse → AI-ready platform transformations.</p><p style="margin: 0px;">Develop code conversion strategies and automated migration frameworks.</p><p style="margin: 0px;">Ensure modernised data platforms are optimised for downstream AI/ML workloads, including feature engineering, model training pipelines, and serving layers.</p><ol start="3"><li>Platform Engineering – Databricks, Snowflake & Google Cloud</li></ol><p style="margin: 0px;">Databricks: Lakehouse architecture, Delta Lake, Unity Catalog, MLflow, Databricks Workflows, and Spark-based processing.</p><p style="margin: 0px;">Snowflake: Data sharing, Snowpark, Streams & Tasks, dynamic tables, Snowflake Cortex, and governance features.</p><p style="margin: 0px;">Google Cloud Data & Analytics: BigQuery, Dataproc, Dataflow, Pub/Sub, Dataplex, Vertex AI integration, and Cloud Composer.</p><p style="margin: 0px;">Build reusable accelerators, templates, and demo environments across these platforms.</p><ol start="4"><li>AI & Machine Learning Integration</li></ol><p style="margin: 0px;">Strong working knowledge of AI/ML concepts, including supervised/unsupervised learning, LLMs, RAG architectures, and GenAI application patterns.</p><p style="margin: 0px;">Design data pipelines and platform architectures that enable AI readiness — clean, governed, feature-rich, and accessible data.</p><p style="margin: 0px;">Demonstrate understanding of MLOps practices including model versioning, experiment tracking, and deployment pipelines.</p><p style="margin: 0px;">Support integration of AI capabilities into solution proposals, such as AI-assisted data quality, intelligent document processing, and predictive analytics use cases.</p><ol start="5"><li>Data Governance & Quality</li></ol><p style="margin: 0px;">Design governance layers within modern platforms (Unity Catalog, Snowflake governance, GCP Dataplex).</p><p style="margin: 0px;">Define data quality frameworks, lineage tracking, and cataloguing strategies as part of solution designs.</p><p style="margin: 0px;">Incorporate privacy, compliance, and security best practices into architectures.</p><ol start="6"><li>Accelerator Development & Thought Leadership</li></ol><p style="margin: 0px;">Build and maintain reusable presales assets: reference architectures, estimation templates, demo scripts, and proof-of-concept kits.</p><p style="margin: 0px;">Contribute to POVs, whitepapers, and technical blogs on data modernisation and AI topics.</p><p style="margin: 0px;">Stay current with platform releases, industry trends, and competitive landscape across Databricks, Snowflake, and Google Cloud.</p><ol start="7"><li>Collaboration</li></ol><p style="margin: 0px;">Work closely with the Americas Presales Lead to ensure alignment on deal strategy and timelines.</p><p style="margin: 0px;">Coordinate with delivery teams to validate solution feasibility and transition smoothly from presales to execution.</p><p style="margin: 0px;">Engage with Databricks, Snowflake, and Google Cloud partner teams for joint solutioning and co-selling activities.</p> <br>Requirements<br><br><p style="margin: 0px;">Required Skills & Experience</p><p style="margin: 0px;">8–15 years of experience in data engineering, data platforms, or analytics, with at least 2 years in a presales or solutioning capacity.</p><p style="margin: 0px;">Hands-on expertise in at least two of the three core platforms: Databricks, Snowflake, and Google Cloud Data & Analytics.</p><p style="margin: 0px;">Proven experience in data warehouse modernisation — migration from legacy platforms (Oracle, Teradata, Netezza) to cloud-native architectures.</p><p style="margin: 0px;">Strong knowledge of AI/ML fundamentals, including GenAI, LLMs, RAG, and MLOps. Ability to articulate how data platforms enable AI outcomes is essential.</p><p style="margin: 0px;">Proficiency in SQL, Python, and Spark. Familiarity with infrastructure-as-code (Terraform) is a plus.</p><p style="margin: 0px;">Experience creating solution architectures, technical proposals, and effort estimations for large deals.</p><p style="margin: 0px;">Excellent communication skills — ability to articulate technical solutions to both technical and business audiences.</p><p style="margin: 0px;">Preferred Qualifications</p><p style="margin: 0px;">Certifications in Databricks (Data Engineer Associate/Professional), Snowflake (SnowPro Core/Advanced), or Google Cloud (Professional Data Engineer / ML Engineer).</p><p style="margin: 0px;">Experience working with Americas or global clients across time zones.</p><p style="margin: 0px;">Exposure to FinOps and cloud cost optimisation strategies.</p><p style="margin: 0px;">Background in telecom, BFSI, or enterprise verticals.</p><p style="margin: 0px;">Familiarity with data mesh and domain-driven data product architectures.</p>