Data and Analytics Specialist

Job Overview / Purpose

We are seeking a highly skilled and hands-on Data and Analytics Specialist to join our growing Data and Analytics team. This role requires strong practical experience designing, developing, troubleshooting, and supporting enterprise data solutions using Snowflake, Azure Data Factory, Azure Data Lake, SQL, Python, and Power BI .

The successful candidate will be expected to become productive quickly within an established enterprise data environment. While business and domain knowledge will be provided through onboarding, the candidate should already possess the technical depth, troubleshooting capability, and independence required to work effectively with complex production data solutions .

This is a hands-on technical delivery role . The successful candidate must be comfortable investigating unfamiliar code and pipelines, tracing data and dependencies across multiple layers, identifying root causes, assessing downstream impacts, and implementing reliable production-ready solutions with limited technical hand-holding.
Key Accountabilities

- Design, develop, optimize, and support robust ETL/ELT pipelines and enterprise data solutions using Azure Data Factory, Azure Data Lake, and Snowflake.

- Independently investigate and troubleshoot complex production issues , tracing data across source systems, ADF pipelines, ADLS, Snowflake objects, transformations, business logic, and downstream reporting.

- Write, read, modify, and debug advanced SQL, Snowflake views, stored procedures, streams, tasks, UDFs, and existing production code , including solutions developed by other team members.

- Perform thorough root-cause analysis for pipeline failures, data discrepancies, performance issues, and unexpected system behaviour, including situations where the failure point is not immediately known.

- Analyze existing data models, SQL logic, pipeline dependencies, transformations, and downstream consumers to determine end-to-end lineage and impact before implementing changes .

- Develop and deploy scalable Python scripts and Azure Functions for automation, integrations, data processing, and advanced analytics use cases.

- Develop and maintain Power BI reports, semantic models, datasets, measures, and transformations , including troubleshooting data and performance issues.

- Translate business requirements into reliable and maintainable technical solutions while proactively identifying technical risks, dependencies, and data-quality concerns.

- Support production data quality, reconciliation, governance, documentation, and metadata management practices.

- Monitor and optimize production solutions for performance, reliability, scalability, automation, and maintainability .

- Contribute to enterprise data models and technical standards while working effectively across both new development and established/legacy solutions .

- Take ownership of assigned technical problems from initial investigation through resolution, validation, deployment, and post-production verification .

Qualifications

Education:

- Undergraduate degree in Computer Science, Engineering, Mathematics, or related STEM field; a Master’s degree is an asset.

Experience:

- Minimum 3–5 years of substantive hands-on experience designing, developing, debugging, and supporting enterprise data solutions using a modern cloud data stack.

- Strong hands-on Snowflake experience, including practical use and troubleshooting of:

SQL and complex transformations

- Views and stored procedures

- Streams and Tasks

- UDFs

- Query execution and performance troubleshooting

- Data lineage and dependency analysis

- Strong practical experience with:

Azure Data Factory

- Azure Data Lake Storage

- Azure Functions

- Advanced SQL development and debugging

- Python for data processing and automation

- Demonstrated experience supporting production data pipelines and business-critical analytics solutions , including independently diagnosing failures and data discrepancies across multiple technology layers.

- Experience working with existing enterprise codebases and unfamiliar data solutions , where investigation and back-tracing are required before changes can be made.

- Snowflake certification (e.g., SnowPro Core or Advanced) is an asset; however, certification does not replace demonstrated hands-on technical proficiency .

Skills & Abilities:

- Strong practical experience with Power BI , including semantic models, Power Query, DAX/measures, troubleshooting, and performance considerations.

- Demonstrated ability to receive an unfamiliar data problem and independently trace the technical flow, isolate the root cause, determine downstream impact, recommend a solution, and implement the fix .

- Strong ability to understand and troubleshoot SQL, Python, ADF pipelines, Snowflake stored procedures, data models, and existing business logic developed by others .

- Strong analytical and problem-solving ability, particularly in situations where requirements are incomplete or the source of an issue is ambiguous.

- Ability to work independently with limited technical supervision after initial business and environment onboarding .

- Experience with DevOps, Git/GitHub, deployment practices, and source control is a strong asset.

- Strong communication skills with the ability to clearly articulate technical findings, solution options, dependencies, and risks to technical and business stakeholders.

- Strong ownership mindset with the ability to manage work from problem identification through production resolution while collaborating effectively within a fast-paced team.

Effort

- Requires sustained mental focus for complex troubleshooting, root-cause analysis, data lineage investigation, coding, pipeline optimization, and report development .

- Requires frequent hands-on use of cloud data platforms, development tools, query interfaces, logs, and monitoring systems for extended periods.

- Requires the ability to move between new development, production support, debugging, and multiple stakeholder priorities.

- High attention to detail is required when modifying existing production solutions, validating data, assessing downstream dependencies, and implementing technical changes.

Working Conditions

- Work is primarily performed in a corporate office environment with the flexibility of a hybrid work model.

- May involve occasional extended hours during production incidents, system deployments, data migrations, or critical project deadlines.

- Requires regular collaboration with cross-functional business and technical teams.

- Must be comfortable working in a fast-paced and evolving enterprise data environment where troubleshooting existing solutions is as important as developing new ones .

*Candidates should be prepared to demonstrate practical problem-solving and hands-on technical proficiency with Snowflake, SQL, and data pipeline troubleshooting as part of the interview process."