Data Analyst/Data Engineer

Job description

Job Summary:

The Data Analyst & Data Engineer will be responsible for collecting, processing, and analyzing data to help drive decision-making processes. This role requires a blend of analytical skills and technical expertise, particularly in cloud environments such as AWS or Azure. The ideal candidate will have a strong background in data analytics, data engineering, and cloud technologies.

Key Responsibilities:

Data Analysis:

- Analyze large datasets using SQL, Python to identify trends, patterns, and insights.- Create and maintain dashboards and reports using data visualization tools like Power BI, Tableau to communicate findings to stakeholders.

- Collaborate with business units to understand their data needs and deliver actionable insights.

Data Engineering:

- Design, develop, and maintain data pipelines using tools like Azure Data Factory, Azure Data Lake, or Azure Synapse Analytics to ensure reliable and efficient data flow.

- Implement data warehousing solutions and ETL processes using technologies such as Azure SQL Data Warehouse (Synapse).

- Ensure data quality and integrity by implementing best practices in data governance and management.

Cloud Technologies:

- Utilize AWS (S3, Redshift, Glue, Athena) or Azure (Data Lake, Synapse, Databricks) cloud platforms for data storage, processing, and analytics.

- Implement and manage cloud-based data solutions, ensuring scalability and security using tools like AWS Lambda, AWS CloudFormation, or Azure Data Factory.

- Stay updated with the latest advancements in cloud technologies and best practices.

Technical Expertise:

- Utilize analytical thinking to approach complex business problems.

- Proficient in tools such as Power BI, SSRS, Azure Synapse Analytics, T-SQL, Python with PySpark or Pandas, and SSAS.

Data Transformation:

- Work on Notebooks (Python) to transform raw data in Synapse using Apache Spark.

- Create and optimize stored procedures, showcasing expertise in handling complex SQL Joins and relationships.

Report Creation:

- Develop measures in Power BI and SSAS, creating interactive and insightful reports in Power BI.

Project Leadership:

- Guide and mentor team members in data analytics projects.

- Demonstrate the ability to work on end-to-end data projects, from raw data transformation to presenting insights.

Collaboration:

- Work closely with data scientists, analysts, and other stakeholders to understand data requirements.

- Provide technical support and training to team members on data tools and technologies.

- Participate in cross-functional projects and contribute to the overall data strategy.

Communication:

- Interact effectively with clients to understand their requirements and provide regular updates.