Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.
Role Overview
Photon is looking for a Senior Data Platform Engineer to support large-scale Fraud and AI-led transformation initiatives within the financial services industry.
This role will focus on building and engineering scalable data platforms that bring together customer, account, transaction, payment, channel, device, behavioral, and fraud-related data across complex enterprise environments.
The ideal candidate will have strong hands-on experience with Big Data platforms, distributed data processing, real-time data pipelines, and large-scale data integration, primarily within on-premise or hybrid enterprise environments.
Key Responsibilities- Design, build, and enhance large-scale data platforms and data pipelines supporting fraud, analytics, AI/ML, and operational use cases.
- Develop pipelines to ingest and process customer, account, transaction, payment, channel, device, and behavioral data from multiple enterprise systems.
- Build batch, near-real-time, and streaming data processing solutions for high-volume and high-velocity data.
- Develop and optimize distributed data processing using technologies such as Spark, Hadoop, Kafka, and related Big Data frameworks.
- Implement scalable data transformation, enrichment, aggregation, and reconciliation processes.
- Build reusable data services and frameworks that enable downstream applications to consume trusted enterprise data.
- Partner closely with Data Architects, Fraud teams, AI/ML engineers, application teams, and business stakeholders to translate architecture into working solutions.
- Implement data models and physical data structures based on defined conceptual and logical models.
- Integrate data across legacy systems, relational databases, enterprise data platforms, APIs, messaging platforms, and distributed data environments.
- Develop and support real-time event-driven pipelines used for fraud detection, monitoring, alerting, and decisioning.
- Improve platform performance, scalability, resiliency, and reliability through tuning and optimization.
- Implement appropriate data quality, lineage, monitoring, reconciliation, security, and operational controls.
- Troubleshoot complex production data issues and drive root-cause analysis and remediation.
- Contribute to engineering standards, reusable components, automation, and platform best practices.
- Strong hands-on experience as a Senior Data Engineer / Data Platform Engineer / Big Data Engineer in large-scale enterprise environments.
- Strong development experience with Apache Spark, including large-scale distributed processing and performance optimization.
- Strong experience with Kafka or equivalent event-streaming technologies.
- Experience working with Hadoop and distributed data ecosystems.
- Strong programming experience in Java, Python, Scala, or similar languages.
- Advanced proficiency in SQL and large-scale data transformation.
- Strong experience building ETL/ELT and streaming data pipelines.
- Experience processing high-volume transactional and event-based data.
- Strong knowledge of data partitioning, parallel processing, caching, performance tuning, and distributed computing concepts.
- Experience integrating data across mainframe/legacy systems, relational databases, APIs, messaging systems, and Big Data platforms.
- Strong understanding of data models, schemas, data structures, and enterprise data integration patterns.
- Experience with production support, monitoring, troubleshooting, and performance optimization of enterprise data platforms.
- Understanding of data quality, lineage, metadata, security, and governance requirements within regulated environments.
- Experience within Banking, Payments, Cards, Fraud, Financial Crime, or Retail Financial Services.
- Experience working with customer, account, transaction, payment, merchant, device, and behavioral data.
- Experience supporting fraud detection, transaction monitoring, risk scoring, investigation, or real-time decisioning platforms.
- Experience building data platforms that support AI/ML models, feature engineering, model scoring, and analytics.
- Experience working in complex on-premise enterprise technology environments.
- Familiarity with large-scale data modernization and platform consolidation initiatives.
A strong hands-on engineer who can build the data platform, not just design it.
The individual should be comfortable taking an architecture or business requirement and translating it into:
Working pipelines, scalable data processing, reliable integrations, performant data structures, and production-grade data services.
The ideal candidate should be equally comfortable writing code, troubleshooting pipelines, tuning Spark jobs, working with Kafka streams, understanding data models, and solving complex enterprise data integration problems
Compensation, Benefits and Duration
Minimum Compensation: USD 38,000
Maximum Compensation: USD 133,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
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