Sr. Data Engineer

Summary As a Senior Data Engineer, you will design, build, and support the core systems that power our data platform to enable fast, data-driven decisions. You will create scalable data pipelines, self-service tools, and governance solutions that ensure trusted, accessible data across the organization. Working closely with business partners and the Data Platform team, you will support advanced analytics and machine learning use cases while sharing knowledge to elevate the team. This role emphasizes scalable pipeline development, distributed data processing, strong data modeling, and engineering best practices. Success requires curiosity, experimentation, and a commitment to operational reliability and team mentorship. Responsibilities Design and build scalable data pipelines to ingest, transform, and curate data from APIs, databases, files, and event streams. Lead technical design reviews and translate complex business needs into enterprise-grade data solutions. Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical) to support analytics, BI, and productized datasets. Champion SDLC best practices, continuous delivery, and infrastructure automation using CI/CD and Infrastructure as Code. Optimize complex distributed workloads using SQL, Python; mentor others on tuning and scalable design patterns. Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity and platform adoption. Perform root-cause analysis for major data incidents, lead long-term remediation, and drive operational reliability improvements. Provide technical mentorship, guide code reviews, and help shape engineering capability maturity. Collaborate with Architects, Data Leads, Product Owners, and cross-functional engineering teams to define long-term data strategies. Perform other duties as assigned. Requirements 5 to 7+ years of experience in data engineering or a related technical field. Expertise in SQL and advanced proficiency in at least one programming language, Python preferred. Strong experience designing and tuning distributed data processing systems at scale. Proven experience designing and implementing complex data models across multiple business domains. Strong knowledge of version control, CI/CD, DevOps/DataOps, automated testing, and engineering best practices. Ability to lead cross-functional engineering initiatives and influence technical roadmaps. Strong problem-solving, debugging, and analytical skills in complex, multi-system environments. Extensive hands‑on experience building scalable pipelines and workflows in Databricks (Delta Lake, Spark, Unity Catalog, Jobs, Workflows). Nice to Have DataOps experience (pipeline observability, monitoring, automated quality). Knowledge of metadata management or cataloging platforms (Purview, Collibra, Alation). Experience with streaming frameworks used with Spark Structured Streaming (Kafka, Event Hubs, Kinesis). Experience working in an Agile environment. Engagement & Logistics Engagement Length: 12 months or more Time Zone: CST (9am - 5pm) Laptop: BYOD. Overtime Required: Very unlikely. In the unlikely event that they ask for some "on call" hours during the week the OT rate is 1.5x the regular rate Selection process Meeting with Resilient Co. team. Client technical interview Project interview (Cultural fit and tech review) Final stage