Experience

professional summary

• i am a data engineer with 3 years of experience in fintech.
• my focus has been on creating reliable aws-based data platforms that help with important financial tasks like tracking profits and losses, managing subscriptions, handling risks, processing payments, and managing digital wallets.
• at two startups, i was the first person to set up these systems, which meant i had to design everything myself.
• i used tools like kafka for real-time data, spark for processing, airflow for scheduling jobs, and tableau for visualizations.
• my work has helped companies make better decisions, detect fraud, and improve efficiency, leading to real business benefits like faster operations and more revenue.

senior data engineer, july 2024 – present

(founding data engineer) at blazpay ai

company overview

• blazpay ai is an ai-driven fintech platform that helps with portfolio management, payments, and cross-border financial services.
• it uses smart tools for analytics, alerts, and integrations to make finance easier and more secure.

key responsibilities and projects

• as the founding data engineer, i was responsible for building the entire data system from nothing.
• this included collecting data, processing it, storing it, and making it useful for the business.

project 1: building the aws data platform from scratch

• what i did:

  • i set up the system to collect events (like user actions or transactions) from sources like dynamodb (a fast database) and sftp (a secure file transfer).
  • i used tools like kinesis, firehose, and kafka to bring in the data quickly.
  • every day, the system handles over 1 million events.

• technical details:

  • i processed this data using spark on glue jobs.
  • this allowed for quick transformations, like analyzing trading sessions, checking for fraud, and executing trades, all with very low delay (less than 150 milliseconds).
  • this made dashboards update almost instantly.

• impact:

  • this enabled real-time analytics for trades and helped detect fraud early, which is crucial in fintech to prevent losses.

• challenges:

  • starting from zero meant deciding on the best tools without any existing setup.
  • i had to ensure it could scale as the user base grew to 50,000 daily active users (dau).

• what i learned:

  • i gained experience in designing systems that handle high volumes of data in real time.
  • it taught me the importance of low-latency processing in financial apps where seconds matter.

project 2: automating etl pipelines

• what i did:

  • i used airflow and glue to automate the data workflows.
  • this included checks for data quality, monitoring for issues, automatic retries if something failed, and reconciliation (matching data from different sources).

• technical details:

  • these automations reduced support tickets by 65%, meaning fewer manual fixes were needed.

• impact:

  • it improved features like subscriptions (recurring payments) and chat-and-pay (ai-based payment tools), making the platform more user-friendly and boosting engagement.

• challenges:

  • integrating with gamification features (like daily tasks to encourage users) required careful planning to keep data flowing smoothly.

• what i learned:

  • automation is key to efficiency.
  • i learned how to build reliable pipelines that run without constant oversight, saving time for the team.

project 3: storage and serving layer design

• what i did:

  • i organized data storage in s3 buckets with zones for raw, processed, and curated data.
  • for queries, i used redshift and athena.
  • i also created tableau dashboards for investors to see key metrics.

• technical details:

  • this supported monitoring kpis like profit/loss, delinquent accounts (late payments), bankruptcy risks, and dormant accounts (inactive users).

• impact:

  • these dashboards helped during investor meetings by showing clear financial health.

• challenges:

  • ensuring data was secure and compliant with fintech regulations.

• what i learned:

  • proper data organization makes analysis faster and more accurate.
  • i understood how zoning data helps in managing large datasets without confusion.

project 4: ci/cd tooling development

• what i did:

  • i built custom tools in python for continuous integration and deployment (ci/cd) of the data pipelines.

• technical details:

  • this reduced deployment time by 40%, meaning updates could go live faster.

• impact:

  • it aligned the technical work with business goals, like revenue growth, and helped in seed funding by proving the system’s scalability.

• challenges:

  • coordinating with the ceo and other teams to prioritize features.

• what i learned:

  • leadership in decision-making and how technical choices directly affect business outcomes, like funding rounds.

overall lessons from blazpay ai

• working here strengthened my skills in ai-integrated fintech.
• i learned to balance speed and reliability in data systems, especially for real-time features.
• being the first engineer built my ownership mindset and taught me cross-team collaboration.

data engineer, december 2022 – june 2024

(software engineer – data) at bks mygold

company overview

• bks mygold is a fintech platform for gold leasing and investments.
• it offers secure management of digital and physical gold assets with guaranteed growth, stored in regulated vaults.
• it connects traditional finance with modern digital tools.

key responsibilities and projects

• as the first and only data engineer, i modernized their old systems into a more efficient etl and analytics setup.
• i handled everything from data collection to reporting.

project 1: modernizing the etl stack

• what i did:

  • i built batch etl pipelines using python, sql, aws glue, and lambda.
  • data came from rds databases (transactional records), payment gateway logs, and customer onboarding forms.

• technical details:

  • i optimized these jobs to cut processing time by 40%, handling tasks like tracking price history, subscription flows, gold investment plans (gip), and finance dashboards.

• impact:

  • this made daily operations smoother and more accurate for financial reporting.

• challenges:

  • dealing with varied data sources that weren’t standardized.

• what i learned:

  • optimization techniques, like tuning queries and jobs, to handle batch processing efficiently.

project 2: implementing real-time streaming

• what i did:

  • i added kafka and firehose for real-time monitoring of transactions.

• technical details:

  • this powered machine learning models for fraud detection with sub-minute delays.
  • i integrated streams and cdc (change data capture) with dynamodb and s3 to manage delinquent payments, dormant accounts, and overall risks.

• impact:

  • it provided quick insights into notifications, authentication, wallet integrations, and marketplace events (like bids and buys).

• challenges:

  • ensuring low latency while maintaining data accuracy in a secure environment.

• what i learned:

  • the value of streaming data in fintech for immediate risk management, like spotting fraud before it escalates.

project 3: data warehousing migration

• what i did:

  • i moved reporting to aws redshift and designed star-schema models for data on transactions, customers, and wallets.

• technical details:

  • this improved query speeds by about 50%, making it easier to analyze large datasets.

• impact:

  • analysts could run reports faster without slowdowns.

• challenges:

  • migrating without disrupting ongoing operations.

• what i learned:

  • data modeling basics, like star schemas, which organize data for better performance in analytics.

project 4: building dashboards and analytics

• what i did:

  • i created tableau dashboards for things like kyc (know your customer) funnels, revenue insights, and gold redemption trends.

• technical details:

  • these provided near real-time access for non-technical teams in product and finance.

• impact:

  • it helped teams make data-driven decisions quickly.

• challenges:

  • making complex data simple and visual for everyone.

• what i learned:

  • visualization tools bridge the gap between tech and business, improving communication.

project 5: automating ci/cd

• what i did:

  • i set up ci/cd using git and jenkins for deploying pipelines.

• technical details:

  • this reduced manual work and sped up releases, supporting secure features like scan-and-pay and subscription renewals.

• impact:

  • created a stable foundation for ml analytics and investor reports that still runs today.

• challenges:

  • owning the entire system as the only engineer.

• what i learned:

  • end-to-end ownership builds resilience and deep understanding of systems.

overall lessons from bks mygold

• this role was my entry into fintech data engineering.
• i learned to modernize legacy systems and the importance of security in asset management.
• it taught me self-reliance and how data drives financial decisions.

skills overview

• technical: building etl pipelines, real-time streaming with kafka, data processing with spark, orchestration with airflow, storage in s3/redshift, and visualizations in tableau.
• also: ci/cd with python, git, and jenkins.
• fintech domains: handling profit/loss, subscriptions, risk (fraud, delinquent accounts), wallets, payments, kpis, and real-time analytics.
• programming: proficient in python and sql.
• architectures: event-driven systems, star-schema modeling, and ml pipelines.

conclusion

• my experience as a data engineer has been about creating value through data in fintech.
• from building systems that detect fraud in real time to dashboards that guide business decisions, i’ve learned that good data engineering combines technical skills with business understanding.
• these roles have prepared me for larger challenges in mncs, where scalability and impact are key.
• i’m excited to bring this expertise to new opportunities.