Senior Consultant - Snowflake Quality Engineering

Your work profile:

The Snowflake quality data engineer is responsible for managing client relationships and overseeing the successful delivery of programs or accounts. This role serves as the primary point of contact between the organization and its clients, ensuring alignment of goals, timelines, budgets, and expected outcomes while driving client satisfaction and business growth.

Key Skills & Competencies:

• 5 to 8 years of substantial hands-on experience delivering production-grade Snowflake platforms and complex data engineering solutions at enterprise scale.
• Advanced SQL expertise, including complex transformations, query profiling, performance diagnosis, and optimization of high-volume workloads.
• Strong Python engineering skills for reusable data-processing components, automation, API integration, testing, and operational tooling.
• Demonstrated experience designing and operating ELT/ETL and orchestration solutions using tools such as dbt, Airflow, Informatica, Matillion, Fivetran, Talend, Azure Data Factory, or AWS Glue.
• Strong experience with at least one cloud ecosystem: AWS (S3, Glue, Lambda, EMR), Azure (ADLS Gen2, ADF, Synapse, Databricks), or GCP (GCS, Dataflow, Dataproc, Pub/Sub).
• Proven capability in data modelling, incremental processing, schema evolution, data quality, metadata/lineage, and production support.
• Experience implementing CI/CD, automated testing, Git-based development workflows, infrastructure as code, and secure cloud engineering practices.
• Ability to lead technical discussions, make architecture trade-offs explicit, communicate risks, and guide engineers through complex delivery challenges.
• Experience with Snowflake cost governance, resource monitors, workload isolation, account-level controls, and platform usage analytics.
• Exposure to Snowflake performance features and advanced platform patterns, including dynamic data refresh, secure sharing, and cross-environment promotion.
• Experience with Databricks, Kafka, event-driven architectures, Data Vault 2.0, or enterprise Lakehouse patterns.
• Working knowledge of data governance and privacy controls, including catalogues, lineage, RBAC, masking, and regulatory requirements.
• Exposure to AI/ML and GenAI data platforms, feature/data preparation, vector-search patterns, or RAG pipelines.
• Banking and Financial Services experience, including regulated data environments, is an advantage.
• SnowPro Advanced or relevant cloud/data engineering certifications.

Responsibilities:

• Own the technical design and implementation of complex Snowflake data solutions, including ingestion patterns, transformation layers, data models, workload design, and platform integration.
• Design robust batch, incremental, and near-real-time ELT/ETL pipelines for structured and semi-structured data using Snowflake, SQL, Python, and enterprise integration/orchestration tools.
• Select and implement appropriate Snowflake capabilities, including Snowpipe, Streams and Tasks, Dynamic Tables, External Tables, Time Travel, Zero-Copy Cloning, and secure Data Sharing, based on workload and architectural requirements.
• Establish reusable data engineering patterns, framework components, naming conventions, quality code standards, testing approaches, and deployment practices.
• Lead advanced data modelling and transformation design across dimensional models, data marts, and, where relevant, Data Vault or Lakehouse patterns; guide adoption of dbt or equivalent tooling.
• Engineer secure integrations across Snowflake and AWS, Azure, or GCP services, addressing data movement, reliability, identity, networking, and operational considerations.
• Drive query and workload optimization through execution-plan analysis, warehouse sizing, workload isolation, caching, clustering, and monitoring; identify and implement cost optimization opportunities.
• Define and implement production-grade observability, data quality, reconciliation, lineage, alerting, incident management, disaster recovery, and service-level controls.
• Set platform security and governance implementation patterns, including RBAC, least-privilege access, masking, secure data sharing, secrets management, and auditability.
• Lead code reviews, technical design reviews, testing strategy, CI/CD implementation, and infrastructure-as-code practices using Git and tools such as Terraform.
• Partner with architects, product owners, business stakeholders, analytics teams, and platform/security teams to shape requirements, assess trade-offs, and convert them into scalable solutions.
• Mentor engineers, unblock complex technical issues, review deliverables, and contribute to estimation, delivery planning, technical risk management, and continuous improvement.

Preferred Qualifications:

• Bachelor's or Master's degree in computer science, Engineering, Information Systems, or a related discipline.
• Relevant professional experience demonstrates both hands-on technical depth and ownership of complex data engineering outcomes.

Success Metrics:

• Strong experience with at least one cloud ecosystem: AWS (S3, Glue, Lambda, EMR), Azure (ADLS Gen2, ADF, Synapse, Databricks), or GCP (GCS, Dataflow, Dataproc, Pub/Sub).
• Lead code reviews, technical design reviews, testing strategy, CI/CD implementation, and infrastructure-as-code practices using Git and tools such as Terraform.
• Design robust batch, incremental, and near-real-time ELT/ETL pipelines for structured and semi-structured data using Snowflake, SQL, Python, and enterprise integration/orchestration tools.
• Proven capability in data modelling, incremental processing, schema evolution, data quality, metadata/lineage, and production support.
• Advanced SQL expertise, including complex transformations, query profiling, performance diagnosis, and optimization of high-volume workloads.
• Strong Python engineering skills for reusable data-processing components, automation, API integration, testing, and operational tooling.
• Demonstrated experience designing and operating ELT/ETL and orchestration solutions using tools such as dbt, Airflow, Informatica, Matillion, Fivetran, Talend, Azure Data Factory, or AWS Glue.
• Lead advanced data modelling and transformation design across dimensional models, data marts, and, where relevant, Data Vault or Lakehouse patterns; guide adoption of dbt or equivalent tooling.

Location and way of working:

• Base location: Hyderabad
• 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.