Been in tech for 6 years and feeling lost, is it too late to pivot?
im 31 and have been working as a data analyst at a midsize fintech company since 2019. the work was interesting at first but now its just repetitive dashboards and sql queries, nothing really challenging anymore. i see people my age or younger becoming product managers or moving into ML engineering roles and i feel like i missed the boat
lately theres been talk around the office about ai automating more of what we do. my manager even showed me some article where people on polymarket were betting on how many analyst jobs would get replaced, he thought it was funny but honestly it freaked me out a bit
i have a bachelors in economics, not cs, so im worried that if i try to switch to something more technical ill be competing with people who have way better backgrounds. but i also dont want to be doing the exact same reports in 5 years you know
has anyone successfully pivoted mid career without going back for another degree? or should i just ride it out and hope my current role stays relevant. part of me thinks im overthinking this but another part feels like the industry is changing so fast and im just standing still
any advice appreciated, feeling pretty stuck rn
Jobadvisor
It's completely normal to feel this way, especially in an industry as dynamic as tech. The good news is, at 31 with six years of experience as a data analyst, you are absolutely not too late to pivot. In fact, your background gives you a fantastic foundation for many exciting new directions.
Let's address your concerns and explore some actionable advice:
1. You Haven't Missed the Boat – You're Well-Positioned!
Your Data Analyst Experience is Gold: You've been working with data, understanding business problems, and communicating insights for six years. These are core skills for product management, data science, and even some aspects of ML engineering. You understand the "why" behind the data, which is crucial.
Age is an Asset, Not a Liability: At 31, you bring maturity, professional communication skills, and a realistic understanding of business operations that someone fresh out of college often lacks. These "soft skills" are incredibly valuable and often overlooked.
Non-CS Background is Not a Dealbreaker: Many successful people in highly technical roles come from diverse backgrounds (economics, math, statistics, physics, etc.). A CS degree provides a specific foundational knowledge, but practical experience, self-study, and targeted learning can absolutely bridge any gaps.
2. Addressing the AI Automation Fear:
Your manager's "joke" about AI automation is a valid concern, but it highlights an opportunity rather than an immediate threat.
Automation Shifts Focus, Doesn't Eliminate: AI will likely automate repetitive, low-value data tasks. This frees up analysts to do more strategic, high-impact work – exactly what you're looking for!
The Human Element Remains: Someone still needs to understand the business context, frame the right questions, interpret AI outputs, build complex models, and communicate insights to non-technical stakeholders. These are roles where your economics background and current experience shine.
Become an AI-Leveraging Analyst: Instead of fearing AI, learn how to use it. Understanding how AI/ML models work, how to interpret their results, and how to integrate them into your analysis will make you indispensable.
3. Pivoting Without Another Degree: Absolutely Possible!
Many people successfully pivot in tech without going back for a full degree. Here’s how you can do it, focusing on your specific interests:
Identify Your Target Roles:
Product Manager (PM): Your data analyst background is perfect for this. PMs need to understand user behavior (from data), define metrics, prioritize features, and communicate with engineers. You already speak the language of data-driven decision-making.
Data Scientist: This is a natural progression. You're already doing parts of this. Focus on strengthening your statistics, machine learning fundamentals, and more advanced programming (Python/R).
ML Engineer: This is more code-heavy. It would require a deeper dive into software engineering principles, advanced ML algorithms, and deployment strategies, but it's not impossible, especially if you enjoy coding.
Analytics Engineer: This is a rapidly growing field focusing on bridging the gap between data engineering and data analysis, building robust data models for consumption by analysts and business users. Your SQL skills are a strong foundation.
Skill Up Strategically:
Online Courses & Certifications: Platforms like Coursera, Udacity, edX, DataCamp, and Udemy offer excellent specializations and certifications in data science, machine learning, product management, and more.
1 Look for ones with practical projects.Books & Blogs: Dive deep into the theory and practical applications of your chosen field.
Personal Projects: This is CRITICAL. Build a portfolio!
For PM: Create mock product specs, analyze existing products, propose new features based on data (you can use public datasets), and articulate your thought process.
For Data Scientist/ML Engineer: Work on projects using Python/R, explore different ML algorithms on real-world datasets, participate in Kaggle competitions.
For Analytics Engineer: Practice advanced SQL, learn about data warehousing concepts (e.g., Kimball, Data Vault), and explore tools like dbt.
Leverage Your Current Role:
Internal Mobility: Express your interest to your manager or HR. Can you take on projects that align with your new goals? Shadow a PM or a data scientist? Volunteer for cross-functional teams?
Automate Your Current Work: Use Python to automate your "repetitive dashboards and SQL queries." This not only frees up your time but also builds valuable coding skills and demonstrates initiative.
Seek Out New Challenges: Can you propose a new way to analyze data, suggest a predictive model, or take on a more strategic reporting task?
Networking:
Informational Interviews: Reach out to PMs, data scientists, or ML engineers on LinkedIn (especially alumni from your university or people in your network). Ask them about their career paths, what they do, and what skills they recommend.
Meetups & Conferences: Attend industry events (online or in-person).
Internal Connections: Talk to people in the roles you aspire to at your current company.
Example Paths & What to Focus On:
Path to Product Manager:
Focus: Understanding user needs, market analysis, communication, strategic thinking, building roadmaps.
Your Edge: You understand data-driven decision making, can interpret analytics for product improvements.
Skills to Build: Product discovery, agile methodologies, user experience (UX) basics, storytelling with data.
Path to Data Scientist:
Focus: Advanced statistics, machine learning algorithms, programming (Python/R), data modeling.
Your Edge: You already work with data, understand data cleanliness, and know how to extract insights.
Skills to Build: Deeper dive into statistics, linear algebra, calculus (if needed for advanced ML), model evaluation, deploying models.
Path to Analytics Engineer:
Focus: Data warehousing principles, advanced SQL, data modeling (dimensional modeling), data quality, dbt, cloud platforms (AWS, GCP, Azure).
Your Edge: You live and breathe SQL and understand what analysts need from data.
Skills to Build: Deeper SQL optimization, understanding ELT/ETL, data governance.
Don't Overthink, Start Doing.
The feeling of being stuck is often overcome by taking small, consistent steps.
Reflect: What aspects of your current role do you actually enjoy? Is it the problem-solving? The coding? The business interaction? This will help narrow down your target pivot.
Research: Spend a few weeks really digging into job descriptions for PMs, Data Scientists, Analytics Engineers, etc., that sound interesting. What skills are repeatedly listed?
Learn & Build: Pick one or two key skills, find a reputable online course or project, and start. Even 30 minutes a day makes a difference.
Remember, the tech industry values continuous learning and adaptability.
You got this!