Data Analytics Lead

Job Summary

We are seeking a highly experienced and visionary Data Analytics Lead to transform complex data into actionable business strategies. With 6+ years of experience, you will serve as the primary bridge between raw data and executive decision-making. You will be responsible for conducting deep exploratory data analysis, uncovering hidden patterns, and using exceptional data storytelling to guide leadership in shaping the future of our business. If you are a master at gleaning profound insights from large datasets and presenting them compellingly to non-technical stakeholders, this role is for you.

Key Responsibilities

1. Exploratory Data Analysis (EDA): Lead complex, open-ended investigations into our enterprise databases to identify trends, anomalies, and opportunities that others might miss.
2. Data Storytelling & Executive Reporting: Translate highly technical findings into clear, compelling narratives. Present strategic insights to C-suite executives and cross-functional leaders using powerful visualizations and presentations.
3. Strategic Insights: Go beyond standard reporting by gleaning proactive insights from data. Formulate hypotheses, design analyses to test them, and deliver recommendations that directly impact revenue, efficiency, and product strategy.
4. Data Architecture & Extraction: Utilize advanced SQL to query, manipulate, and extract data from complex relational databases and data warehouses. Advise on database design and data modeling to ensure analytical readiness.
5. Technical Leadership & Mentorship: Serve as the subject matter expert in analytics methodologies. Mentor junior and mid-level analysts, elevating the team's overall capabilities in EDA, coding, and business acumen.

Required Qualifications

1. Experience: 6+ years of dedicated experience in data analytics, business intelligence, or data science roles.
2. Core Analytics Skills: Master-level proficiency in Exploratory Data Analysis (EDA) and a proven track record of gleaning measurable business insights from complex, messy datasets.
3. Communication: Exceptional data storytelling skills. Must be able to distill complex statistical concepts into plain business language for non-technical audiences.
4. Database & SQL Mastery: Expert-level knowledge of SQL (e.g., PostgreSQL, MySQL, Snowflake, BigQuery) and a deep understanding of relational databases, data warehousing concepts, and data pipelines.

Preferred Qualifications

1. Advanced Programming: Strong proficiency in R or Python (Pandas, NumPy, Matplotlib, Seaborn) for advanced data manipulation, statistical analysis, and predictive modeling.
2. Education: Bachelors or Masters degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
3. Business Acumen: Previous experience working within Retirement service industry and a deep understanding of industry-specific KPIs will be an added advantage