Saurabh Singh

Technical Architect · Intuitive

March 2025 – Present · | Hyderabad

• Responsibilities:

SAURABH SINGH

• Strategic Data Engineering Leader: Proven expertise in driving business transformation through innovative, data-driven solutions across the Technology, Telecom, Retail, and Healthcare domains, with a strong focus on solution architecture and scalable implementations.
• Multi-Cloud & Lakehouse Expertise: Deep proficiency across Google Cloud Platform (BigQuery, Dataflow, Composer), AWS, Databricks, and Snowflake — architecting lakehouse and warehouse platforms that process terabyte-scale workloads daily with measurable gains in analytics and operational decision-making.
• Large-Scale Migration Leadership: Led end-to-end migration of legacy on-premise warehouses (Teradata, Oracle, SAP, Hadoop) to modern cloud platforms, moving 280+ TB cumulatively with zero critical data loss and consistently on-schedule cutovers.
• Impactful Optimization: Expert at optimizing operational efficiency with robust data quality frameworks and FinOps practices that have improved data accuracy by 37% and reduced cloud costs by up to 42%.
• Team Leadership & Innovation: Directed and mentored high-performing teams of up to 12 data engineers, cultivating a collaborative culture that drove innovation, continuous improvement, and delivery of cost-effective, scalable data solutions.
• Business-Driven Product Development: Orchestrated the development of a consumer-facing product leveraging geospatial data, resulting in a measurable increase in user engagement and showcasing the tangible business impact of strategic data engineering initiatives.
• Architecting end-to-end data migration programs that move legacy on-premise warehouses (Teradata, Oracle, SQL Server, Hadoop) to Snowflake, Databricks Lakehouse, and Google
• BigQuery — covering current-state assessment, schema conversion, code remediation, and cutover planning.
• Defining cloud-agnostic reference architectures across GCP, AWS, and Azure, enabling clients to standardise ingestion, storage, transformation, and consumption patterns across heterogeneous platforms.

Designing medallion (bronze / silver / gold) lakehouse architectures on Databricks with Delta

• Lake, implementing ACID-compliant incremental loads, schema evolution, and time-travel- based recovery.
• Building high-throughput ETL and ELT pipelines using PySpark, Databricks Workflows, dbt, Dataflow, AWS Glue, and Snowflake Snowpark to serve enterprise analytics and machine- learning workloads.
• Leading Snowflake platform design — virtual warehouse sizing, resource monitors, clustering keys, Snowpipe-based continuous ingestion, and Streams & Tasks for change data capture.
• Orchestrating complex dependency-driven workflows with Apache Airflow (Cloud Composer and Amazon MWAA), introducing reusable DAG templates and SLA-based alerting across 400+ pipelines.

Implementing real · time ingestion using Apache Kafka, Pub/Sub, Kinesis, and Spark Structured

• Streaming to support sub-minute operational reporting and event-driven downstream triggers.

Establishing CI/CD for data platforms with GitHub Actions · Terraform, and Databricks Asset

• Bundles, bringing infrastructure, pipeline code, and warehouse objects under version control.
• Developing automated data validation and reconciliation frameworks performing row-count, checksum, and column-level comparisons between source and target systems during every migration wave.
• Defining data governance and security standards including Unity Catalog, Snowflake RBAC, dynamic data masking, row-access policies, encryption at rest and in transit, and GDPR / HIPAA alignment.
• Driving performance tuning and FinOps across platforms through partitioning, clustering, caching, auto-suspend policies, workload isolation, and query profiling of long-running jobs.
• Partnering with client CxO and data leadership stakeholders on solution design, effort estimation, RFP responses, and multi-quarter migration roadmaps.