Senior Data Engineer

As a Senior Data Engineer, you will design, develop, and operate data platform components and data-intensive products. You will own significant data engineering work from requirements and design through implementation and production support, partnering with Data Engineering, Data Science, Cloud, DevOps, Application, Product, and client-facing teams to deliver reliable solutions that support business growth. This is a hands-on role for an experienced engineer who can solve distributed-data challenges, contribute to sound technical decisions, support other engineers, and help bring emerging technologies into reliable production capabilities. Responsibilities Design, develop, and maintain data pipelines, platform components, and data-intensive services. Own data engineering work from requirements and technical design through implementation, deployment, and production support. Contribute to data acquisition strategies, dataset contracts, acceptance criteria, quality standards, lineage, and lifecycle policies. Build scalable batch and real-time data pipelines, lakehouse architectures, and distributed processing solutions for high-volume workloads. Help evolve the data ecosystem to support growth in data volume, velocity, variety, and product complexity. Apply and improve reusable patterns for data ingestion, transformation, storage, orchestration, testing, observability, and delivery. Build production systems that meet expectations for reliability, security, performance, scalability, cost efficiency, and SLA-driven delivery. Contribute to operational practices for monitoring, incident response, failure recovery, replay, backfills, schema evolution, and disaster recovery. Partner with Data Science and ML teams to productionize ML, AI, and LLM-based workloads and integrate them into customer-facing products. Collaborate with Cloud, DevOps, Application, Security, Product, and client teams to deliver robust solutions for business and customer needs. Evaluate technologies and architectural patterns, contribute to technical proofs of concept, and recommend adoption based on measurable value. Document technical decisions, trade-offs, risks, and implementation approaches. Participate in technical reviews and help improve design, code quality, testing, maintainability, and production ownership. Support engineering roadmaps, hiring, and capability development across the data engineering discipline.

6+ years of hands-on software or data engineering experience, including experience designing, building, and operating production-grade data systems. Experience contributing to complex data initiatives across teams. B.Sc. or M.Sc. in Computer Science, Computer Engineering, or an equivalent technical field, or comparable practical experience. Strong understanding of data-intensive and distributed systems , including scalability, reliability, consistency, and performance trade-offs. Strong proficiency in Python and working proficiency in Scala. Strong expertise with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform. Experience with large-scale data processing technologies such as Apache Spark and Databricks. Experience designing and operating event-driven or streaming systems using technologies such as Apache Kafka. Experience with workflow orchestration and platform tooling such as Apache Airflow , including maintainable DAGs and operational business-critical workflows. Advanced SQL skills and strong experience with data modeling , query optimization, and data storage systems. Hands-on experience with at least one of the following: data lake or lakehouse architectures; columnar or analytical databases; or NoSQL or distributed database systems. Experience owning production-grade batch and/or streaming pipelines, including data quality, observability, schema evolution, backfills, recovery procedures, and SLA management. Hands-on experience designing, developing, deploying, and operating production-grade microservices.