Career Strategy Plan
Practical Applications
To convert our job market analytics into actionable career recommendations, we followed a structured three-step framework:
1. Market Demand Extraction
We analyzed Lightcast job postings for Data & Analytics roles across high-demand sectors (Information, Finance & Insurance, and Professional Services).
2. Team Capability Assessment
Each member evaluated their skill levels (1–5) across seven core analytics competencies.
3. Gap & Alignment Strategy
By comparing industry demand vs. team proficiency, we identified skill strengths, shortages, and role-alignment recommendations.
Individual Strategy Profiles
Skill Profile (Interactive Radar Chart)
Dinara’s Improvement Plan
AWS Skills
Focus on expanding cloud fundamentals beyond EC2, next steps include S3, IAM, Lambda, and RDS. The goal is to build a small end-to-end pipeline on AWS and publish it with diagrams and documentation.
Visualization Tools (Tableau & Power BI)
Increase hands-on experience with dashboard tools by recreating datasets into visual stories. Dashboards will be shared on Tableau Public and Power BI, with short summaries of insights and methodology.
Portfolio Development
Strengthen the portfolio through project-based learning: data pipelines, SQL analyses, interactive dashboards, and business analytics mini-projects. Each project will be fully documented on GitHub and added to the website.
Python & SQL Consistency
Maintain steady practice in core programming skills through SQL challenges, exploratory notebooks, and small automation scripts. Emphasis on clean code, reproducibility, and clarity.
Professional Branding
Enhance visibility through consistent GitHub activity, project write-ups, and LinkedIn updates. Integrate learning progress and completed dashboards into the website to show growth over time.
Skill Profile (Interactive Radar Chart)
Interpretation & Career Strategy
Cloud & Data Engineering Readiness
I plan to extend my cloud skills by moving into services that support analytics workflows, including S3 for storage, IAM for access management, and Lambda for automation. Applying these tools to a small pipeline inspired by the data work I’ve done before will help solidify the concepts. Clear diagrams and documentation will reinforce the design.
Visualization & Analytical Storytelling
To strengthen how I communicate insights, I’ll continue developing dashboards that turn complex datasets into clear visual narratives. Rebuilding analyses in Tableau or Power BI and adding concise explanations of the methods and findings will help sharpen both structure and storytelling.
Business Analytics Portfolio Growth
I plan to expand my portfolio through projects that reflect my background in macro analysis and data-driven interpretation. Creating well-documented mini-studies on topics like inflation signals, labor patterns, or pricing behavior will highlight both technical ability and business reasoning. Each will include reproducible code and clear takeaways.
Python & SQL Development
I want to keep improving my coding habits through regular practice in both Python and SQL. Writing more organized scripts, refining queries, and automating small tasks related to my coursework or past analytics work will strengthen consistency and technical depth.
Professional Presence & Branding
I’ll enhance my professional presence by sharing dashboards, code, and summaries on GitHub, LinkedIn, and my personal website. Bringing together my coursework, macro insights, and analytics projects in one place will help present a cohesive view of my development.
Skill Profile (Interactive Radar Chart)
Interpretation & Recommendations
As I think about my current skill profile, I’m beginning to understand more clearly how my strengths connect with the kind of work I want to pursue in data and analytics. Skills like SQL, Python, and Excel have become the backbone of the way I approach problems, and they give me the confidence to work through analytical workflows as well as explore the early stages of data engineering. I’m also becoming more comfortable with AWS, and as I continue learning it, I can see how valuable this will be as cloud technologies become more standard across the industry.
At the same time, this project made it very clear where I need to grow next. Visualization tools such as Tableau and Power BI show up in almost every job posting I look at, and developing stronger skills in these areas would help me tell better stories with data and communicate my ideas more effectively. I also want to deepen my knowledge of AWS, especially services like S3, Lambda, Glue, and Redshift, because understanding how these pieces fit together will allow me to build more complete, automated, and scalable analytics pipelines.
Looking ahead, I can see myself moving toward a more technical analytics path, whether that means becoming a Technical Data Analyst, an Analytics Engineer, or even a Junior Data Engineer. To prepare for that future, I want to strengthen my abilities in Tableau and Power BI so I can improve the way I communicate insights, and I also want to advance my AWS proficiency by eventually earning the relevant certifications. I plan to start building more portfolio projects that combine SQL, Python, cloud services, and automation, and I also want to keep improving the way I translate technical findings into meaningful insights that people can act on.
Overall, this project helped me recognize not only the skills I already have, but also the areas where I want to push myself so I can grow into the professional I hope to become. By focusing on visualization and deepening my cloud engineering experience, I feel more confident that I can continue developing into a strong, well-rounded analytics professional who can work across both technical and business environments.
Team Findings
Shared Strengths
- Strong analytical and SQL foundations
- Balanced Python and Excel experience
- Diverse BI + cloud skills across members
Shared Growth Opportunities
- AWS for all members
- Advanced SQL
- Dashboard portfolio development