AVP, Principal Data Engineer (L11)

Relha LLC; India; On-site; INR 1,000,000 - 2,500,000; Full time

Benefits offered by this job

Enhanced flexibility

Work from home option

Regional hubs in India

Job summary

Synchrony India is seeking an AVP/ Principal Data Engineer to lead hands-on development of cloud-native, event-driven data platforms. You will own end-to-end data pipeline design for real-time transactional workflows and AI-ready data products, modernizing legacy systems and shaping enterprise data architecture.

You will guide agile teams, establish coding standards, CI/CD, and DevOps practices, and mentor engineers while collaborating with architects and product managers to align delivery with

Qualifications

• 8+ years in ELT/ETL and data warehousing or Lakehouse engineering.
• Experience with Ab Initio, Kafka, Spark, Hive, and AWS services.
• Proven ability to lead or mentor data engineering teams in SAFe/Agile.

Responsibilities

• Design, develop and own production-grade data engineering apps with end-to-end ownership.
• Build scalable ELT/ETL pipelines and 24x7 real-time streaming using Kafka/Spark.
• Own event-driven data workflows for high-throughput financial data.
• Lead cloud migration to modern, cloud-native architecture.
• Collaborate with solution architects and product owners in SAFe delivery.

Skills

ELT/ETL

Data Warehousing

Lakehouse

Kafka/MSK/Confluent

Spark Streaming

AWSS Cloud

SAFe/Agile

Mentoring

Leadership

Education

Bachelor’s degree in Computer Science/Engineering

Tools

Ab Initio

Hive

Oracle/MySQL

AWS EMR/Glue/S3/Redshift

Job description

Company Overview: Synchrony (NYSE: SYF) is a leading consumer financing company that has been at the heart of American commerce and opportunity for nearly a century. Synchrony delivers credit and banking products that empower tens of millions of consumers to improve their financial lives and access what matters most. Leveraging innovative solutions that are shaping the future of retail commerce, Synchrony supports the growth and success of some of the nation’s most respected brands, alongside hundreds of thousands of small and midsize businesses, including health and wellness providers. Committed to excellence in service and culture, Synchrony is proud to be named as #3 as a Great Place to Work® in India and is honored to be ranked the #1 Best Company to Work For® in the U.S. by Fortune magazine and Great Place to Work®. For more information, visit www.synchrony.com.

Organizational Overview:

Synchrony's Data Engineering team architects and operates cloud-native data platforms and event-driven pipelines that process hundreds of millions of credit card transactions annually, delivering curated, trustworthy datasets to downstream consumers and operational systems with high availability and strong reliability requirements

We champion engineering rigor, data quality, and a platform-first mindset across real-time event streaming, batch orchestration, Lakehouse architecture, and AI-ready data products, leveraging cutting-edge open-source and cloud-native technologies.

Role Summary/Purpose:

We are looking for an AVP, Principal Data Engineer to serve as a hands-on technical leader who both drives and delivers, writing production-quality code, owning end-to-end data pipeline design and delivery, and setting the technical direction for the team. This role will lead the design and delivery of cloud-native, event-driven data platforms that power real-time transactional workflows and AI-ready data products, while modernizing legacy data systems and influencing architectural decisions across Synchrony’s enterprise data platform.

Key Responsibilities:

Design, develop, and lead production-grade data engineering applications across agile teams, combining hands-on coding with technical leadership, design reviews, and end-to-end delivery ownership.

Design and implement scalable ELT/ETL and 24x7 real-time event streaming pipelines using Ab Initio Continuous Flows/Spark Streaming and Kafka ecosystems (AWS MSK and Confluent Kafka), ensuring fault tolerance, strong delivery guarantees (idempotency/exactly-once where applicable), and near-zero downtime deployments.

Own event-driven data workflows that process high-throughput, low-latency financial transactions across mission-critical systems, and build/operate Lakehouse data infrastructure on AWS (S3, Glue, EMR, Redshift), using Ab Initio (must-have) and Spark as core engineering tool.

Develop AI-ready data products and feature pipelines for downstream analytics and operational AI use cases, and lead cloud migration efforts to modernize legacy batch ETL into cloud-native, event-driven architecture.

Collaborate with solution architects to translate designs into robust, scalable implementations; partner with product owners and program managers in a SAFe/Scaled Agile environment to align delivery with business priorities.

Define and enforce data engineering standards, coding best practices, CI/CD, and DevOps processes; mentor engineers and foster a culture of engineering excellence and continuous learning.

Required Skills/Knowledge:

Bachelor’s degree in Computer Science, Engineering, or related field with 8+ years of experience in ELT/ETL, data warehousing, or Lakehouse engineering; or, in lieu of a degree with 10+ years of equivalent hands-on experience.

8+ years of full lifecycle data engineering experience (design, development, testing, documentation, performance tuning, and production support) across large-scale technologies including Ab Initio, Kafka (MSK/Confluent), Spark, Hive, RDBMS (Oracle/MySQL), and AWS services (EMR, MSK, Glue, S3, Redshift, IAM).

5+ years of experience with 24x7 real-time/streaming pipelines and 2+ years with AWS cloud platform engineering, including infrastructure-as-code and containerized workloads.

Experience leading or mentoring data engineering teams in an Agile/SAFe delivery environment.

Ability to travel for business as needed.

Desired Skills/Knowledge:

Proven experience as a hands-on data engineering technical lead in a SAFe environment, owning mission-critical data platform applications with end-to-end accountability across batch and real-time pipelines in a DevOps model.

Experience in FinTech, credit card, or banking environments handling sensitive data in a regulated context, with the ability to communicate complex technical topics and influence decisions across IT and business stakeholders.

AWS Solutions Architect or AWS Data Engineer certification (or actively pursuing). Exposure to agentic AI or LLM-integrated data workflows is a plus.

Thought leadership such as articles, publications, or open-source contributions in data engineering or cloud data platforms is a plus.

Eligibility Criteria:

Bachelor's degree in Computer Science, Engineering, or related field with 8+ years of experience in ELT/ETL, Data Warehousing, or large-scale data platform engineering on distributed systems and public cloud (AWS preferred).

In lieu of a degree with 10+ years of equivalent hands-on experience in large-scale data environments.

Work Timings: 3:00 PM to 12:00 AM IST

This role qualifies for Enhanced Flexibility offered in Synchrony India and will require the incumbent to be available between 06:00 AM Eastern Time – 11:30 AM Eastern Time (timings are anchored to US Eastern hours and will adjust twice a year locally). This window is for meetings with India and US teams. The remaining hours will be flexible for the employee to choose. Exceptions may apply periodically due to business needs) We are proud to offer flexibility at Synchrony. Our way of working allows you the option to work from home or workspaces in our Regional Engagement Hubs—Hyderabad, Bengaluru, Pune, Kolkata, or Delhi/NCR. Occasionally you may be required to commute or travel to Hyderabad or one of the Regional Engagement Hubs for in person engagement activities such as business or team meetings, trainings, and culture events.

Must not be any corrective action plan (First Formal/Final Formal, LPP)

L09+ Employees who have completed 18 months in the organization and 12 months in current role and level are only eligible