SENIOR DATA & ML PLATFORM ENGINEER

Why Flow

Unique opportunity to join a global fintech start-up and be part of the core-team that is

building a global SaaS platform for the world’s 17,000 banks, 40+ million merchants and

7Bn cardholders. Flow Networks o ers a white-label payment data activation platform to

banks and merchants to engage their customers at the payment moment.

Role Overview

Working Closely with the Business, Product, and Solution Architect, the Data & ML

Platform Engineer will design and operationalize Flow’s data and ML foundation—spanning

batch and streaming pipelines, Build ML Models, ML data assets, model pipelines, and

business analytics and measurement systems. Work directly with the Engineering and

Data Leaders including CTO and Chief Data & Analytics O icer to priorities the

development and deployment of the action plans. You will shape how data is captured,

moved, transformed, combined, and used to power insights, models, and features across

the organization.

Key Responsibilities


Own the design, development, and operation of Flow’s data and ML platform,

including real time and batch pipelines for ingestion, transformation, feature

engineering, and model serving using Databricks, Spark, Delta Lake, Kafka, and BI

tools like PBI, Tableau.


Build high quality, analytics ready ML datasets, feature pipelines, and

training/evaluation frameworks that power product features such as

personalization, eligibility logic, frequency control, and next best action decisioning.


Partner closely with Product, GTM, and Engineering teams to translate product

requirements into scalable data, ML, and analytics assets, including feature

definitions, model outputs, and business metrics that directly influence product

behavior and customer engagement.


Own and evolve the data catalog, semantic layer, metric definitions, and ML

governance standards, ensuring consistency, discoverability, versioning, lineage,

and trust across datasets, features, and models.


Design data, experimentation, and measurement systems, including test/control

assignment, exposure tracking, causal impact analysis, outcome attribution, and

incremental lift frameworks to enable scientific product decision-making.

https://flownetworks.io/

Investigate, reconcile, and resolve data or model quality issues, proactively

improving reliability, observability, drift monitoring, data validation, and end-to-end

performance of both data and ML pipelines.


Contribute to end-to-end machine learning workflows by building feature stores,

training datasets, automated training pipelines, CI/CD based model deployment

workflows, and real time/batch inference systems used for personalization and

decisioning.


Continuously identify platform gaps, performance bottlenecks, and architectural

improvement opportunities, driving enhancements in data scalability, ML pipeline

automation, system reliability, and developer productivity.

You Should Have


Bachelor’s degree in computer science, Engineering, or equivalent practical

experience.


5+ years in data engineering, analytics engineering, or data science with strong

ownership of production data systems.


Deep hands-on experience with distributed data processing (Spark), modern data

platforms (Databricks), and lake house architecture (Delta Lake).


Experience with streaming pipelines (Kafka, Spark Streaming, Flink) and event

driven data architectures.


Strong collaboration with product and engineering teams, shaping data

requirements and influencing product behavior.


Solid understanding of experimentation, incrementality, and measurement

frameworks in customer engagement systems.


Handson experience with ML workflows, feature engineering, training pipelines, and

model evaluation.


High degree of autonomy, strong debugging skills, and a continuous improvement

mindset.


Understanding of Mar Tech Platform, Customer lifecycle, Personalization at Scale

will be an added advantage.