Join our Technology & Transformation practice and help organizations leverage vast data resources. We guide companies in managing diverse data sources, transforming them into actionable insights for data-driven decision-making and achieving a competitive advantage. Our expertise spans business intelligence, data management, performance management, and advanced analytics, including big data, cloud, cognitive, and machine learning technologies.

This role focuses on managing client relationships and ensuring the successful delivery of data engineering programs. You will be the key liaison between Deloitte and clients, aligning goals, timelines, budgets, and expected outcomes to foster client satisfaction and business growth.

Develop and maintain Snowflake data warehouse and Lakehouse components, including databases, schemas, and secure data access patterns. Build and operate batch and incremental ELT/ETL pipelines for structured and semi-structured data using SQL and Python. Implement advanced Snowflake features like Snowpipe, Streams, Tasks, and Data Sharing. Develop reusable transformations and dimensional models, utilizing frameworks like dbt. Integrate Snowflake with cloud storage services (AWS, Azure, GCP) for secure data ingestion. Troubleshoot pipeline failures, reconcile data, and implement data quality checks. Optimize SQL queries and workloads for performance and cost efficiency. Implement robust data protection controls, including role-based access and masking. Utilize Git, code reviews, automated testing, and CI/CD practices for production-ready data assets. Collaborate with stakeholders to clarify requirements and deliver tested data solutions.

Possess 4-6 years of experience in Snowflake data warehousing and Lakehouse development. Proficiency in building and operating ELT/ETL pipelines with SQL and Python. Experience implementing Snowflake features such as Snowpipe, Streams, Tasks, and Time Travel. Familiarity with transformation frameworks like dbt and integration with cloud storage (AWS, Azure, GCP). Skilled in troubleshooting pipeline issues, data reconciliation, and implementing data quality checks. Expertise in SQL query tuning, workload optimization, and cost management. Experience with data security implementation, including RBAC and masking. Proficiency in Git, code reviews, and CI/CD practices. Strong collaboration skills with data architects, analysts, and business stakeholders. Hands-on experience with Snowflake core objects, loading patterns, and operational troubleshooting. Advanced SQL skills including complex joins, CTEs, window functions, and performance tuning. Working proficiency in Python for data processing and automation. Practical experience with ETL/ELT or orchestration tools (e.g., dbt, Airflow). Experience with at least one major cloud platform (AWS, Azure, GCP). Understanding of relational databases and semi-structured formats like JSON and Parquet. Familiarity with Git, code review, CI/CD, testing, and secure cloud development practices. Ability to translate technical requirements into data pipeline deliverables. Exposure to Databricks, Kafka, and lakehouse architectures is beneficial. Familiarity with data governance, metadata, and lineage concepts. Awareness of AI/ML and GenAI data requirements. A Bachelor's or Master's degree in a relevant technical field is preferred.