Snowflake, Dbt, Python, Airflow

Role Proficiency:
Provide expertise on data analysis techniques using software tools. Under supervision streamline business processes.

Outcomes:

- Design and manage the reporting environment; which include data sources security and metadata.
- Provide technical expertise on data storage structures data mining and data cleansing.
- Support the data warehouse in identifying and revising reporting requirements.
- Support initiatives for data integrity and normalization.
- Assess tests and implement new or upgraded software. Assist with strategic decisions on new systems. Generate reports from single or multiple systems.
- Troubleshoot the reporting database environment and associated reports.
- Identify and recommend new ways to streamline business processes
- Illustrate data graphically and translate complex findings into written text.
- Locate results to help clients make better decisions. Solicit feedback from clients and build solutions based on feedback.
- Train end users on new reports and dashboards.

Set FAST goals and provide feedback on FAST goals of repartees

Measures of Outcomes:

- Quality - number of review comments on codes written
- Data consistency and data quality.
- Illustrates data graphically; translates complex findings into written text.
- Number of results located to help clients make informed decisions.
- Number of business processes changed due to vital analysis.
- Number of Business Intelligent Dashboards developed
- Number of productivity standards defined for project

Number of mandatory trainings completed

Outputs Expected:
Determine Specific Data needs:

- Work with departmental managers to outline the specific data needs for each business method analysis project

Critical business insights:

- Mines the business’s database in search of critical business insights; communicates findings to relevant departments.

Code:

- Creates efficient and reusable SQL code meant for the improvement
manipulation
and analysis of data.
- Creates efficient and reusable code. Follows coding best practices.

Create/Validate Data Models:

- Builds statistical models; diagnoses
validates
and improves the performance of these models over time.

Predictive analytics:

- Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis

Prescriptive analytics:

- Attempts to identify what business action to take

Code Versioning:

- Organize and manage the changes and revisions to code. Use a version control tool for example git
bitbucket. etc.

Create Reports:

- Create reports depicting the trends and behaviours from analyzed data

Document:

- Create documentation for worked performed. Additionally
perform peer reviews of documentation of others' work

Manage knowledge:

- Consume and contribute to project related documents
share point
libraries and client universities

Status Reporting:

- Report status of tasks assigned

Comply with project related reporting standards and processes

Skill Examples:

- Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
- Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
- Critical Thinking: Data Analysts must review numbers trends and data to come up with original conclusions based on the findings.
- Presentation Skills - facilitates reports and oral presentations to senior colleagues
- Strong meeting facilitation skills as well as presentation skills.
- Attention to Detail: Vigilant in the analysis to determine accurate conclusions.
- Mathematical Skills to estimate numerical data.
- Work in a team environment

Proactively ask for and offer help

Knowledge Examples:

- Database languages such as SQL
- Programming language such as R or Python
- Analytical tools and languages such as SAS & Mahout.
- Proficiency in MATLAB.
- Data visualization software such as Tableau or Qlik.
- Proficient in mathematics and calculations.
- Efficiently with spreadsheet tools such as Microsoft Excel or Google Sheets
- DBMS
- Operating Systems and software platforms

Knowledge regarding customer domain and sub domain where problem is solvedAdditional Comments:

- Experience in python, specifically in the area of data engineering in a commercial setting (e.g. hands on coding experience) (pandas (or dask, vaex etc), Airflow) - Good understanding of ETL/ELT patterns, idempotency and other data engineering best practices. - Extensive Experience with data modelling (3rd normal form, star schemas, wide/tall projections) - Experience of dealing with Metadata and best practices for cataloguing datasets in Snowflake/other Warehouses - Excellent SQL knowledge, including an understanding of how to write optimised SQL code, good general knowledge of different SQL engines, and what considerations they bring when optimising. - Experience with integrating DataWarehouse/Data pipelines with Data Governance tools like Collibra - Familiar