T&T_Customer_M&C - Senior Consultant | ML/AI/Data Science | Delhi
- Job requisition ID : 104942
- Location: Delhi
- Entity: Deloitte Touche Tohmatsu India LLP
The team
Customer has to do much more than keep the wheels turning; it is the engine that drives functional excellence and the enabler of innovation and long-term growth. Learn more about: Customer
Key Responsibilities:
We are seeking an experienced Data Engineer with 4+ years of experience to design, build, and optimize scalable data pipelines, data models, and analytics-ready datasets. This role is central to enabling our Data Science and Analytics teams — acting as the primary data provider, ensuring they always have clean, curated, and trustworthy data to build models, run experiments, and generate insights. You will work at the intersection of data engineering and data science, bridging raw source systems and analytical consumption layers through a modern cloud-first stack anchored in Snowflake.
Data Pipeline Development & Maintenance:
Design, build, and maintain scalable batch and near real-time data pipelines optimized for data science consumption patterns.
Develop robust ETL/ELT workflows using Snowflake Streams, Tasks, and Dynamic Tables to ingest and transform data from multiple sources.
Ensure pipelines deliver low-latency, high-freshness data feeds for model training, feature engineering, and reporting.
Automate pipeline workflows and implement orchestration using Airflow or equivalent tools with full lineage tracking.
Monitor and alert on pipeline health, SLA breaches, and data drift to proactively resolve issues before they impact downstream teams.
Snowflake Data Platform Engineering:
Serve as the primary Snowflake platform engineer — owning schema design, warehouse sizing, resource monitors, and cost governance.
Design and implement dimensional models (star/snowflake schemas) and analytics-ready data layers in Snowflake to support both BI and ML use cases.
Build and maintain curated Gold/Semantic layers in Snowflake using SQL-based transformation logic, ensuring models are tested, documented, and version-controlled.
Leverage Snowflake-native features including Dynamic Tables, Materialized Views, and Search Optimization for ML and analytics workloads.
Implement Snowflake data sharing, secure views, and row-access policies to enable governed, compliant data distribution across teams.
Optimize query performance through query profiling, clustering keys, partitioning strategies, and virtual warehouse auto-scaling.
Manage Snowflake environments across dev/staging/prod, enforcing RBAC, network policies, and data masking for sensitive fields.
Evaluate and adopt new Snowflake capabilities (e.g., Cortex AI/ML Functions, Apache Iceberg integration) to improve platform maturity.
SAS to Snowflake Migration & Modernisation:
Lead the end-to-end migration of legacy SAS-based data pipelines to a modern Snowflake-native architecture — translating SAS DATA steps, PROCs, and macros into performant SQL and Python-based ELT workflows.
Re-engineer SAS transformation logic into Snowflake Tasks and Streams, preserving business rules and output fidelity while significantly improving execution speed, scalability, and maintainability.
Establish a migration validation framework to reconcile SAS outputs against Snowflake equivalents row-by-row — ensuring zero regression in data quality and giving business stakeholders confidence to sign off on cutover.
Data Science & Analytics Enablement (Primary Focus):
Act as the dedicated data provider for the Data Science team — understanding their feature requirements and translating them into reliable, performant datasets.
Build and maintain feature stores and curated feature tables in Snowflake to support reproducible ML model training and inference pipelines.
Design experiment-ready datasets with proper train/test splits, time-based partitioning, and versioning to ensure ML model integrity.
Collaborate with data scientists to understand analytical use cases and pre-aggregate or denormalize data to reduce query complexity for notebooks and dashboards.
Provide clean, structured, and high-quality data for statistical analysis, predictive modeling, and reporting — proactively flagging data anomalies.
Assist in feature engineering pipelines by building reusable transformation logic in Snowpark or Snowflake Tasks that data scientists can directly consume.
Support A/B testing and experimentation infrastructure by ensuring variant-split data is cleanly captured, versioned, and accessible.
Data Quality & Governance:
Implement end-to-end data validation checks using Great Expectations or custom Snowflake-native validation frameworks.
Enforce data accuracy, completeness, freshness, and consistency SLAs across all pipelines serving data science and analytics consumers.
Maintain data contracts between source systems and downstream consumers, documenting schemas, update frequencies, and known limitations.
Work with stakeholders to define and enforce data standards, naming conventions, and governance best practices across the Snowflake platform.
Build and maintain a data catalog and lineage documentation so data scientists can self-serve and understand data provenance.
Performance Optimization & Cost Efficiency:
Continuously profile and tune Snowflake queries used by data science notebooks, dashboards, and scheduled jobs.
Identify and resolve pipeline bottlenecks, redundant materializations, and inefficient storage patterns to reduce Snowflake compute costs.
Refactor legacy pipelines for scalability, applying modern ELT patterns and incremental loading strategies.
Implement table clustering, partitioning, and micro-partition management strategies to optimize large-scale analytical queries.
Collaboration & Stakeholder Management:
Work cross-functionally with Data Scientists, Analysts, Product, and Engineering teams to translate business questions into data solutions.
Participate in sprint planning and backlog grooming with the data science team to prioritize data needs alongside engineering work.
Communicate effectively with both technical and non-technical stakeholders — explaining trade-offs, timelines, and data limitations clearly. Required Skills & Qualifications Experience
4+ years of experience as a Data Engineer or similar role supporting analytics/data science teams. Core Skills
Advanced Snowflake expertise: data modelling, performance tuning, Streams/Tasks, Dynamic Tables, RBAC, data sharing, cost monitoring.
Strong ETL/ELT pipeline experience — building production-grade, monitored, and well-documented workflows.
Solid SQL skills including window functions, CTEs, recursive queries, and performance optimization.
Experience building and maintaining datasets specifically consumed by data science and ML workflows. Programming
Proficiency in Python for data pipeline scripting, automation, and Snowpark-based transformations.
Location and way of working:
Base location: Delhi
Education: Professional Qualification - B.E./B.Tech/MCA/MBA/MS
This profile involves occasional travelling to client locations.
Hybrid is our default way of working. Each domain has customized the hybrid approach to their unique needs.
