Senior Software Engineer, Data Platform

Description

• Ensure data reliability and resilience, implementing redundancy, checkpointing, and replay strategies for real-time and near-real-time data systems.
• Conduct regular performance, scalability, and cost-efficiency audits of data pipelines and warehouses, proactively identifying and resolving bottlenecks or inefficiencies.
• Evolve the data platform leveraging AWS services such as Kinesis/Kafka, Glue, EMR, Lambda, and Redshift to support streaming, batch, and analytical workloads at scale.
• Establish and enforce data quality, validation, and observability standards, building automated checks and alerts using CloudWatch, Datadog and custom frameworks.
• Design and implement robust data ingestion and transformation pipelines, ensuring reliability, performance, and schema consistency across diverse data sources.
• Instrument and monitor data infrastructure using CloudWatch, CloudTrail, and AWS Config, ensuring transparency, traceability, and operational health of data workflows.
• Mentor other engineers and promote best practices in data architecture, pipeline design, and development across the engineering organization.
• Define and maintain data governance and security best practices, including IAM-based access control, encryption policies, and compliance with internal data standards.
• Collaborate with Data Science, ML, business and product teams to model multi-modal data effectively, enabling self-service analytics and high-impact insights across the organization.
• Stay current with AWS and data engineering advancements, evaluating new tools and services to continuously improve scalability, developer velocity, and data accessibility.
• Optimize data storage and lifecycle management through intelligent S3 partitioning, versioning, and lifecycle policies to balance performance, cost, and retention.
• Lead root-cause analysis and incident response for data platform issues, driving long-term reliability improvements and knowledge sharing across teams.

Requirements

• Experience with infrastructure-as-code (Terraform or CloudFormation) and CI/CD automation.
• 6+ years of experience in software or data engineering, focused on large-scale big-data systems and low latency systems.
• AWS or data engineering certifications are a strong plus.
• Solid understanding of data modeling, ETL/ELT design, and distributed data processing (Spark or Flink).
• Excellent collaboration and communication skills, with experience working cross-functionally with analytics and platform teams.
• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Familiarity with data governance, lineage, and observability best practices.
• Strong proficiency in Python, Scala or Java for data processing and automation.
• Hands-on experience with AWS data services such as Kinesis/Kafka, Glue, EMR, Iceberg, Redshift, S3, and Lambda.
• Working knowledge of cloud security, IAM, and cost optimization in AWS.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance