Foundation AI - Lead Data Analyst - Business Intelligence

Designation : Lead Data Analyst

Location : Hyderabad

Work Mode : Work From Office

About US :

Foundation AI automatically ingests incoming documents, emails, and attachments from across your firm. It profiles matches, classifies, and saves each to your DMS, and then automates document-dependent workflows according to your rules.

Job Summary :

- Dashboard and Report Development : Lead the design and creation of intuitive, impactful dashboards, reports, and interactive visualizations using BI tools such as Metabase, Power BI, Tableau, and Looker. Ensure that these tools effectively represent data-driven insights to stakeholders across various departments.

- Advanced Data Extraction & Analysis : Write complex SQL queries to extract, clean, and transform data from various sources, ensuring that the information is accurate and actionable.

- Insight Generation : Conduct thorough analysis on product usage, user behavior, and model performance metrics. Transform raw data into clear, actionable insights that directly influence product and business strategy.

- Data Modeling & Optimization : Design and implement robust data models that support reporting and analysis needs, ensuring scalability and performance optimization.

2. Team Mentoring and Leadership :

- Strategic Vision and Leadership : Set the strategic direction for the data analytics team, aligning their efforts with the organizations broader business objectives. Drive a data-centric culture within the team and the wider organization, championing data-driven decision-making.

- Mentorship & Development : Provide leadership and mentorship to a small team of data analysts and BI developers, fostering professional growth and technical skill development. Encourage best practices, collaboration, and continuous improvement within the team.

- Cultivating Team Culture : Build a team culture that prioritizes learning, innovation, and collaboration, promoting a strong sense of ownership and accountability among team members.

- Technical Guidance : Offer hands-on support to help team members solve complex analytical challenges and improve their technical competencies.

- Innovation : Continuously evaluate and integrate new BI technologies and methodologies to enhance data analysis capabilities and maintain a competitive edge.

- Strategic Problem-Solving : Demonstrated ability to think strategically and navigate complex problems, translating data insights into actionable business strategies.

3. Analytics Strategy & Execution :

- Cross-Department Collaboration : Partner with teams across Product, Engineering, Finance, and other business functions to identify key analytics use cases, define success metrics, and track progress against strategic objectives.

- Roadmap Development : Develop and maintain a clear and actionable analytics roadmap that balances short-term project needs with long-term strategic goals. Regularly review and adjust priorities based on business needs and emerging opportunities.

- Data-Driven Culture Advocacy : Champion a data-driven mindset across the company by promoting the effective use of analytics tools, methodologies, and processes. Encourage stakeholders to leverage data to drive decisions and measure success.

4. Data Infrastructure & Governance

- Collaboration with Data Engineering : Work closely with the Data Engineering team to ensure that data pipelines and data warehouse architecture are robust, scalable, and aligned with the needs of the business.

- Data Quality & Governance : Advocate for and implement best practices in data governance, security, and quality management. Ensure the consistency, accuracy, and reliability of data across all systems and reports.

- Data Security & Compliance : Ensure compliance with data protection regulations and security protocols, safeguarding sensitive data and promoting ethical data practices across the organization.

5. Cross-Functional Collaboration

- Partnership with Finance : Work closely with the Finance team to provide accurate usage data for billing and revenue operations, ensuring the financial integrity of reports and forecasts.

- Customer Success Support : Provide analytical support to the Customer Success team by generating ad-hoc reports and custom analysis to assist with key client interactions and service optimization.

- Collaboration with Product & Engineering : Collaborate with the Product and Engineering teams to improve data collection methods and enhance product analytics capabilities. Advocate for tracking improvements to better measure and understand user behavior and engagement.

6. Stakeholder Communication

- Data Presentation : Present analytical findings, insights, and strategic recommendations to senior leadership in clear, concise, and compelling ways. Tailor presentations to both technical and non-technical audiences.

- Subject Matter Expertise : Act as the subject matter expert (SME) on data analysis tools, techniques, and methodologies, providing support and guidance to internal stakeholders across various departments.

- Business Impact Communication : Communicate how data insights translate into business outcomes, fostering a deeper understanding of the value of analytics in driving company growth and operational success.

Required Qualifications :

Education :

- Bachelors or Masters degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a closely related discipline.

Experience :

- 5+ years of hands-on experience in data analytics, business intelligence, or a related field, with a proven track record of transforming data into actionable insights that drive business outcomes.

- 1+ years of experience in a leadership or mentoring capacity, demonstrating the ability to guide and nurture junior team members while cultivating a collaborative team environment.

Technical Skills :

- BI Tools Expertise : Advanced proficiency with BI tools such as Metabase, Power BI, Tableau, or Looker, with a strong ability to develop interactive dashboards and reports that support data-driven decision-making.

- SQL Proficiency : Expertise in writing complex SQL queries to manipulate large datasets, extract meaningful insights, and drive strategic decision-making.

- Data Warehousing & ETL/ELT : Solid understanding of data warehousing concepts, data modeling, and ETL/ELT pipelines. Familiarity with cloud-based platforms such as Redshift, Snowflake, or BigQuery is a plus.

- Advanced Analytics : Familiarity with advanced analytics techniques or experience with programming languages such as Python or R for data science and machine learning applications is highly desirable.

Soft Skills :

- Communication & Presentation : Exceptional communication skills with the ability to present complex findings and analyses in a clear and concise manner to both technical and non-technical audiences.

- Leadership & Mentorship : Strong leadership capabilities, with a proven ability to inspire, guide, and mentor team members, fostering professional growth and technical development.

- Project Management & Organization : Strong organizational and project management skills, with the ability to manage multiple competing priorities in a fast-paced, dynamic environment.

- Collaboration : Ability to work cross-functionally with diverse teams, build strong relationships, and influence key stakeholders in driving data initiatives across the company.

Nice-to-Have :

- Experience working with machine learning or advanced statistical modeling.

- Exposure to additional analytics platforms or tools (e.g., DataRobot, LookML, etc.).

- Knowledge of data visualization best practices and storytelling techniques to make data insights more accessible and actionable