Tech Lead - Snowflake + SQL + SSIS

8-10 Years

Job Title: Senior Data Engineer (Snowflake and MS SQL Server)

Job Summary

Must-Have Skills & Experience:

8 10 years of professional experience in data engineering.

Deep expertise in Microsoft SQL Server (T-SQL, indexing, query tuning, stored procedures).

Strong hands-on knowledge of Snowflake (database design, ingestion, data sharing, security, and resource management).

Proven experience designing and implementing end-to-end data warehousing and ETL/ELT solutions.

Prior exposure to SSIS package review, analysis, and troubleshooting.

Strong understanding of data architecture, performance tuning, and optimization strategies.

Experience in leading or mentoring developers and providing architectural guidance.

Excellent communication and stakeholder management skills with the ability to translate technical requirements into business-aligned solutions.

Key Responsibilities:

Lead the design and development of data pipelines and integration flows across SQL Server and Snowflake.

Review and guide developers on SSIS package optimization and troubleshooting.

Set coding and design best practices for SQL and Snowflake development, ensuring performance, scalability, and robustness.

Mentor junior and mid-level data engineers by providing technical guidance, peer reviews, and knowledge sharing.

Collaborate with solution architects and business stakeholders to design data models, schemas, and warehouse solutions for enterprise analytics.

Drive initiatives to ensure data quality, governance, and compliance across platforms.

Partner with cross-functional teams to prioritize and deliver strategic data engineering solutions.

Troubleshoot complex issues, conduct root cause analysis, and propose long-term improvements.

Contribute to the continuous improvement of data engineering processes, tools, and Frameworks.

Domain/ Industry Knowledge:

• Working knowledge of customers business processes and relevant technology platform or product.


• Able to analyze current-state and define to-be processes in collaboration with SME and present recommendations with tangible benefits.

Requirement Gathering and Analysis:

• Specialized knowledge of requirement management processes and requirement analysis processes, tools & methodologies.


• Extract requirements for complex scenarios and prototype independently.


• Identify modules impacted, features/functionalities impacted and arrive at high level estimates.


• Develop traceability matrix and identify transition requirements.

Product/ Technology Knowledge:

• Implement code or configure/customize products, drive adoption of industry standards and practices, and contribute to development of reusable assets and innovative solutions.


• Conduct technical sessions and knowledge sharing sessions, and work on complex modules independently.


• Analyze various frameworks/tools and present recommendations, contribute to development of training and certification material, and demonstrate thought leadership through whitepapers and webinars.

Architecture tools and frameworks:

• Specialized knowledge of architecture Industry tools & frameworks.


• Implement tools & framework in a complex scenario.


• Conduct tools & framework customization & tailoring workshop.

Architecture concepts and principles:

• Specialized knowledge of architectural elements, SDLC, methodologies & customer business/domain.


• Establish Architectural principles/ patterns and use advanced tools to capture and analyze system/ technical issues.

Analytics Solution Design:

• In Depth knowledge of statistical & machine learning techniques.


• Able to design analytical modelling approach for moderate-scale projects or for components of large-scale.


• Understand business requirements & constraints including potential trade-offs between speed & accuracy, maintains trends.

Tools & Platform Knowledge:

• Familiarity with the wide range of data science/analytics commercial and open source software tools, their constraints, advantages, disadvantages, areas of application and mainstream packages relevant to technical stages of data science/analytics projects.


• Intermediate to advanced skills in programming languages used for data science/analytics and ability to apply these for data acquisition, pre-processing, modelling and model deployment. Ability to interpret and modify existing scripts and conduct quality checks.

Accountability:

• Takes responsibility for and ensures accuracy of results and deadlines of the function and/or team and in completing own work.

Collaboration:

• Reaches out to others in team to ensure connections are made and team members are working together.


• Looks for ways to integrate work with other teams, identifying similarities and opportunities, making necessary changes in work to ensure successful integration.

Agility:

• Demonstrates openness to the possibilities that change presents and begins to plan for how role may change.


• Works with others to prepare for change.

Customer Focus:

• Identifies trends and patterns emerging from customer preferences and works towards customizing/ refining existing services to exceed customer needs and expectations.

Communication:

• Communicates well-organized ideas, information, and data to broad and diverse audiences across the Organization (through formal and informal presentations).


• Helps others identify their appropriate audience.

Drives Results:

• Sets realistic stretch goals for self & others and perseveres to follow through with resilience and remains calm in a crisis or stressful situation to exceed organization/client expectation.

Resolves Conflict:

• Identifies and understands the source of conflict, addresses, and overcomes.