Position Details: Senior Business Consultant - Data Management

Location: San Francisco, CA

Responsibilities: 

Data Collection and Integration:

•Develop and maintain ETL (Extract, Transform, Load) processes to collect and integrate data from multiple sources (databases, APIs, third-party platforms, etc.).

•Ensure data quality and accuracy by identifying and correcting inconsistencies and gaps in data sources.

•Work with other teams to ensure data is organized, structured, and ready for analysis.

Data Analysis and Insights:

•Analyze large datasets to identify trends, patterns, and insights that support business objectives.

•Collaborate with stakeholders to understand business problems and translate them into data-driven solutions.

•Provide actionable insights that help improve decision-making and drive strategic initiatives.

Data Visualization:

•Design and develop interactive dashboards, reports, and visualizations to communicate insights effectively using tools like Tableau, Power BI, or other BI platforms.

•Ensure that reports and dashboards are aligned with business goals and provide key performance indicators (KPIs) for various departments.

•Continuously enhance the visual appeal and usability of data visualizations to increase their impact.

Data Pipeline and Model Development:

•Build and optimize data pipelines for performance, scalability, and reliability.

•Work on the design and development of data models to support predictive analytics, business intelligence, and machine learning applications.

•Implement best practices for version control, data governance, and data security.

Collaboration with Cross-functional Teams:

•Partner with data scientists, analysts, and business teams to ensure that insights are effectively integrated into workflows.

•Provide data engineering expertise to support machine learning models and algorithms.

•Communicate findings and insights clearly to both technical and non-technical stakeholders.

Continuous Improvement:

•Stay up-to-date with industry trends and best practices in data engineering and analytics.

•Continuously improve data infrastructure and pipelines to handle growing data volumes and complexity.

•Contribute to the development of data policies and procedures to ensure consistent data usage across the organization.

Qualifications:-

Educational Background:

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related field (Master's degree preferred).

Must have minimum of 10 years of total experience

Technical Skills:

•Strong experience with data engineering tools and frameworks (e.g., Apache Spark, Apache Airflow, Kafka, etc.).

•Proficiency in SQL and experience working with large-scale relational and NoSQL databases.

•Familiarity with cloud data platforms (AWS, Azure, GCP) and cloud-based data storage solutions (e.g., S3, BigQuery, Redshift).

•Experience in building and maintaining data pipelines using ETL tools (e.g., Talend, Informatica, or custom Python scripts).

•Knowledge of data modeling and schema design best practices.

•Strong understanding of data wrangling and cleansing techniques.

•Familiarity with data visualization tools (e.g., Tableau, Power BI, Looker).

•Experience with version control systems like Git.

Soft Skills:

•Strong problem-solving skills and the ability to work with complex data.

•Ability to identify trends, outliers, and correlations within large datasets.

•Experience with statistical analysis, machine learning, and predictive modeling is a plus.

•Strong written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders.

•The ability to collaborate effectively across teams and functions to understand data requirements and translate them into solutions.

Experience:

•Experience with programming languages like Python, R, or Scala for data processing and analysis.

•Experience with distributed computing frameworks (e.g., Hadoop, Spark).

•Familiarity with DevOps practices, including continuous integration and deployment (CI/CD) for data pipelines.

•Exposure to data privacy and security regulations (e.g., GDPR, CCPA).

Work Environment:

•Collaborative and fast-paced work environment.

•Opportunity to work with state-of-the-art technologies.

•Supportive and dynamic team culture

#LI-AD1