Cambridge Wealth (Baker Street Fintech)

Department

Product & Technology

Location

On-site | Prabhat Road, Pune

Experience

3-5 Years in a Data Engineering or Analytics Role

Domain

Fintech / Wealth Management — non-negotiable

Compensation

11-12 LPA Fixed + Performance Bonus

Growth

Title upgrade + salary revision at 12–18 months for strong performers

Why this role is different from most Data Engineer postings

You will work directly with the founding team on a live wealth management platform used by HNI and NRI clients. You will not spend years in a queue waiting to matter your work ships to production, your analysis influences product decisions, and you will guide junior teammates from day one. If you perform, a raise and title upgrade are on the table within 1218 months. This is the kind of early-team role that defines careers.

About Cambridge Wealth

Cambridge Wealth is a fast-growing, award-winning Financial Services and Fintech firm obsessed with quality and exceptional client service. We serve a high-profile clientele NRI, Mass Affluent, HNI, and ultra-HNI professionals and have received multiple awards from major Mutual Fund houses and BSE. We are past the zero-to-one stage and now focused on scaling our features and intelligence layer. You will be joining at exactly the right time.

What You Will Be Doing

This is a central, hands-on data engineering role at the intersection of financial analytics and applied ML. You will own the data pipelines and analytical models that power investment insights for wealth management clients transforming transaction data and portfolio information into measurable, actionable intelligence.

We are not looking for someone who just keeps the lights on. We want someone who looks at a working system and immediately sees how to make it 10x faster, cleaner, and smarter using AI and automation wherever possible.

Key Responsibilities:

Data Engineering & Pipelines

• Build and optimize PostgreSQL-based pipelines to process large volumes of investment transaction data.
• Design and maintain database schemas, foreign tables, and analytical structures for performance at scale.
• Write advanced SQL — window functions, stored procedures, query optimization, index design.
• Build Python automation scripts for data ingestion, transformation, and scheduled pipeline runs.
• Monitor AWS RDS workloads and troubleshoot performance issues proactively.

Financial Analytics & Modelling

• Develop analytical frameworks to evaluate client portfolios against benchmarks and category averages.
• Build data models covering mutual fund schemes, SIPs, redemptions, switches, and transfer lifecycles.
• Create materialized views and derived tables optimized for dashboards and internal reporting tools.
• Analyse client transaction history to surface patterns in investment behaviour and financial discipline.

Applied ML & AI-Driven Development

• Use Python (Pandas, NumPy, Scikit-learn) for trend analysis, forecasting, and predictive modelling.
• Implement classification or regression models to support financial pattern detection.
• Use AI tools — LLMs, Copilots — to accelerate ETL development, code quality, and data cleaning.
• Identify opportunities to automate repetitive data tasks and advocate for smarter tooling.

Data Quality & Governance

• Own data integrity end-to-end in a live, high-stakes financial environment.
• Build and maintain validation and cleaning protocols across all financial datasets.
• Maintain Excel models, Power Query workflows, and structured reporting outputs.

Collaboration & Junior Mentorship

• Work directly with Product, Investment Research, and Wealth Advisory teams.
• Translate open-ended business questions into structured queries and measurable outputs.
• Guide 1–2 junior trainees — review their work, set code quality standards, and help them grow.
• Present findings clearly to non-technical stakeholders — no jargon, just clarity.

Skills — What We Need vs. What Helps

Skill / Tool

Requirement

Must-Haves:

SQL & PostgreSQL (window functions, stored procedures, optimization)

Python — Pandas, NumPy for data processing and automation

ML fundamentals — classification or regression (Scikit-learn)

AWS RDS or equivalent cloud database experience

Financial domain knowledge — mutual funds, SIPs, portfolio concepts

Python data visualization — Matplotlib, Seaborn, or Plotly

Strong Advantage

Excel — Power Query, advanced modelling

Materialized views, query planning, index optimization

Experience with BI/dashboard tools

Good to Have

NoSQL databases

Prior fintech or wealth management startup experience

Financial Domain — Non-Negotiable

This is a wealth management platform. You must come in with a working understanding of:

• Mutual fund structures, scheme types, and NAV-based transactions
• Investment lifecycle — SIPs, Lump Sum, Redemptions, Switches, and STPs
• Portfolio allocation and benchmarking against indices (e.g. Nifty 50, category averages)
• How HNI/NRI clients interact with financial products differently from retail investors

You do not need to be a CFA. But if mutual funds and portfolio analytics are completely new territory, this role is not the right fit right now.

The Culture Fit — Read This Carefully

We are a small, fast-moving team. This is not a place where you wait for a ticket to arrive in your queue. The right person for this role:

• Has worked at a small startup before and is used to wearing multiple hats
• Finds broken or slow data systems genuinely irritating and fixes them without being asked
• Reaches for Python or an LLM when there is a repetitive task — automating is instinctive
• Is comfortable saying 'I don't know but I'll find out' and follows through independently
• Wants visibility and ownership, not just a well-defined job description
• Is looking for a role where strong performance is directly visible and rewarded

Growth Path — What Happens If You Perform

This is not a vague 'growth opportunity' pitch.

If you hit the bar in your first 12–18 months, you will receive a salary revision and a title upgrade to Senior Data Engineer or Lead Data Engineer depending on team expansion. As we scale our Data and AI team, this role is the natural stepping stone to a team lead position. You will also gain direct exposure to founding-team decision-making — the kind of access that is hard to get at larger companies.

Preferred Background

• 2–4 years in a data engineering or analytics role at a startup or small Fintech
• Experience in a live product environment where data errors have real consequences
• Exposure to portfolio analytics, investment research, or wealth management platforms
• Has mentored or reviewed code for at least one junior team member

Hiring Process

We respect your time. The process is direct and moves fast.

• Screening Questions — 5 minutes online
• Online Challenge — MCQ(Data, SQL, AWS, etc), and one applied ML or analytics problem, Communication Skills and Personality (focused, not trick questions)
• People Round — 30-minute video call, culture and communication
• Technical Deep-Dive — 1 hour in person, live financial data problems and your past work
• Founder's Interview — 1 hour in person, growth conversation and mutual fit
• Offer & Background Verification