2 Biostadt Jobs

Functional Experience Requirements:

• SQL: Expertise in writing complex SQL queries for data extraction, manipulation, and analysis (MySQL, PostgreSQL, SQL Server, etc.).
• Data Warehousing: Amazon Redshift (fully managed data warehouse), RDS (relational databases), and Amazon S3 (as a data lake with tools like Athena and Redshift Spectrum).
• ETL Tools: AWS Glue (most popular for managed ETL), Lambda, EMR (for big data), Step Functions (for workflow orchestration), and Kinesis (for real-time ETL).
• Data Architecture: Proficiency in data modelling, schema design, and optimization of data architectures.
• Cloud Technologies: Experience with cloud platforms like AWS Cloud for data storage, processing, and deployment.
• Programming: Proficiency in Python, Java, or Scala for data processing.

Key Responsibilities:

• Data Architecture Design:

Design, develop, and maintain scalable, high-performance, and reliable data architectures to support business analytics and data analysis workflows.

Collaborate with data analysts, data scientists, and business teams to understand data needs and build robust data solutions.

• Data Pipeline Development:

Build, test, and maintain data pipelines for batch and real-time data processing, ensuring data quality and consistency across all stages.

Integrate data from a variety of internal and external sources (structured and unstructured data) into a unified data repository.

• ETL Process Implementation:

Design and implement ETL (Extract, Transform, Load) processes to automate the ingestion, transformation, and loading of data from multiple sources into the data warehouse.

Optimize ETL workflows to handle large volumes of data and ensure the efficient movement of data from source systems to target systems.

• SQL Expertise:

Develop complex SQL queries to extract, manipulate, and analyze data, ensuring optimal performance for reporting and analytical purposes.

Conduct data analysis to support business intelligence efforts, using SQL and other tools to generate meaningful insights for decision-making.

• Data Quality & Governance:

Implement data quality checks, error handling, and validation rules to ensure the integrity and accuracy of data pipelines.

Collaborate on establishing data governance practices, ensuring compliance with data privacy and security regulations.

• Collaboration and Reporting:

Work closely with data scientists, business analysts, and other stakeholders to translate business needs into technical solutions.

Provide ongoing support for business analytics teams by delivering data in a clean, structured, and accessible format.