You will lead the design, development, and scaling of a modern data platform, shaping data infrastructure, driving governance, and building scalable APIs that power real-time and batch analytics. You will play a pivotal role in evaluating, designing, and migrating the organization toward a North Star data architecture that supports scalable, secure, and intelligent data operations across the enterprise. You will support analytics use cases across telecom networking, product intelligence, financial reporting, and internal/external data insights, and help build a comprehensive Customer 360 platform powered by ML models and behavioral data, enabling fraud detection, brand intelligence, and personalized customer engagement.
Responsibilities
- Architect, build, and scale a unified data platform integrating internal and external sources into data lakes and warehouses
- Design and implement streaming and batch data pipelines using tools like Spark, Airflow, and DBT
- Lead infrastructure provisioning using Terraform and Kubernetes, ensuring scalable and secure deployments
- Evaluate and drive the migration to the North Star data architecture, aligning platform capabilities with long-term business goals
- Collaborate with cross-functional teams to define logging standards, data contracts, and consumption patterns
- Drive best practices in data governance, privacy compliance, and metadata management
- Build APIs and data services to support real-time activation and client-facing data sharing
- Participate in strategic architectural decisions and long-term data roadmap planning
- Evangelize modern data platform and engineering practices across the organization
- Mentor junior engineers and contribute to hiring and team growth
Requirements
- 15+ years of experience in software engineering with a strong focus on data systems and platform architecture
- 10+ years of experience building data solutions using data lakehouse architectures and self-serve data platforms
- 10+ years of hands-on programming experience with Scala, Go, Python, and SQL
- 5+ years of experience building scalable data solutions using Python, Spark, and Terraform
- Proven experience building serverless and scalable ML infrastructure for large-scale data processing
- Deep expertise in cloud-native data stacks (AWS, Databricks, Snowflake, BigQuery, Synapse)
- Strong understanding of microservices architecture, event-driven systems (Kafka), and container orchestration (Kubernetes)
- Experience building operational tools and defining best practices, including evaluating and adopting AI-based tools
- Experience with orchestration tools like Airflow or Prefect and transformation tools like DBT
- Proficient in building and scaling APIs (REST/gRPC) for data access and activation
- Familiarity with front-end technologies and frameworks (e.g., React) is a plus
- Built distributed Kafka pub/sub model for data ingestion pipeline with queue workers deployed on AWS ECS
- Automated monitoring, disaster recovery, CI/CD supported with filebeat/cloudwatch serverless monitoring
- Hands-on experience with data visualization and BI tools (Tableau, PowerBI, Cibe.dev)
- Strong grasp of data governance, compliance frameworks, and secure data sharing practices
- Experience applying AI/ML and LLMs to data products and predictive analytics
- Exceptional communication and leadership skills in fast-paced environments
Benefits
- Stock option incentive program
- Company paid healthcare
- Flexible work arrangements
- Company sponsored team-lunches and company retreats