This is a remote position.

Supercharge Your Career as a Senior Data Engineering Manager at Technoidentity!

Are you ready to solve people challenges that fuel business growth? At Technoidentity, we’re a Data+AI product engineering company building cutting-edge solutions in the FinTech domain for over 15 years—and we’re expanding globally. It’s the perfect time to join our team of tech innovators and leave your mark!

What’s in it for You?

The Core Datasets team within our Data Engineering organization is at the heart of how our company manages and scales its finance and non-finance data infrastructure. Our data engineers build and maintain the pipelines that power critical business decisions across the company. This includes finance and non finance business use cases and data pipelines that power their P0 needs (like how is the company adopting AI across Engg and Non Engg functions). You'll be joining a collaborative, cross-functional team that partners closely with finance stakeholders, analytics teams, and data owners across the organization.

The Difference You Will Make As a Senior DE Platform Manager, you will sit at the intersection of product management and technical leadership — guiding a pod of data engineers to deliver high-quality, reliable pipelines across finance and non-finance data domains. You will own the roadmap, ensure engineering excellence, and be the primary accountability point for pod delivery and output, driving measurable impact on data quality and pipeline reliability at scale. At the same time, we want the candidate to bring in fresh ideas on how to leverage and implement AI agents at scale to solve complex data quality challenges around detective monitoring, data integrity, completeness, accuracy OR on how to accelerate current Core Datasets pipeline maintenance/ enhancements through AI agents

What Will You Be Doing?

Lead and manage a pod of data engineers working on Core Datasets pipelines, providing technical and logical guidance on pipeline design, data modeling, and architecture decisions
Own and maintain a healthy, prioritized Jira backlog; manage sprint planning, dependency tracking, and delivery milestones to ensure engineers always have clear, unblocked work
Partner closely with finance and non finance stakeholders to gather, document, and prioritize data requirements; collaborate with non-finance teams to deliver analytics and data pipelines as needed
Review and validate pipeline logic, data quality rules, and transformation specs authored by the engineering team
Ensure pipelines are designed to scale and align with the company's evolving data infrastructure, including the migration to Data Morph
Quickly triage and mobilize the right team members when pipeline failures occur
Balance roadmap items across finance-related and non-finance data pipelines to ensure continuous, well-sequenced delivery
In parallel, work with DE on AI Ideation, experimentation, POCs maturing them to production ready deliverables.

Requirements

What Makes You the Perfect Fit?

Proven experience managing or leading a team of data engineers, with strong technical fluency in pipeline design, data modeling, and data quality practices
Demonstrated ability to own and manage a complex, multi-stakeholder engineering roadmap
Experience with sprint planning, backlog management, and delivery tracking (Jira or equivalent)
High level business understanding of finance processes around Treasury Intercompany, Collections, Revenue Operations, Cash Operations, Indirect Taxes.
Prior experience working in high volume B2C platform in a platform manager role is a plus
Strong understanding of data infrastructure and warehouse concepts around big data, data lakes, star schema, batch processing.
Previous experience working with file systems like AWS (with DE teams).
Familiarity with big data file formats like Hive, Iceberg
Basic understanding of coding platforms/ languages like Spark/ Scala, Data Morph, dbt, or similar transformation frameworks is a plus

Excellent cross-functional communication skills — able to partner effectively with finance and non-finance stakeholders to translate business needs into engineering requirements
Comfortable operating in a fast-paced environment with shifting priorities and multiple concurrent workstreams

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