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# Senior Data Engineer

- **Company:** Tamara, Inc
- **Location:** Morrison, TN, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Analysis, Apache HTTP Server, BigQuery, Cloud Computing, Cloud Database, Data Architecture, Information Engineering, Data Infrastructure, Data Systems, Data Warehousing, Distributed Computing Environment, GNU Compiler Collection, Online Analytical Processing, Prometheus, Data Streaming, Parquet, Cloud Platform System, Grafana, Apache Spark, Multi-Cloud, Backend, Data Lakes, Debezium, Kubernetes, Druid, Apache Flink, Apache Kafka, Data Management, Vertica, Terraform, Data Pipelines
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-engineer-morrison-tn--eee5c881-eaaf-45b4-b7d4-24bf532e570b

## About the Role

Healthcare, Investment Funds, GNU C Compiler, Retail, Cloud Computing, Team Building, Data Warehousing, Replication and Remote Mirroring, Apache Kafka, Data Management, Our stack today: GCP · BigQuery · Kafka/Redpanda · Debezium · dbt · Spark · Airflow · Terraform · Kubernetes

 * Preferably 6+ years of experience in data engineering or distributed backend systems, including 3+ years operating large-scale streaming or data platform infrastructure in production, and experience mentoring or technically leading engineers.
 * Hands-on experience with streaming platforms (e.g., Kafka) and distributed processing tools (e.g., Spark or Flink) - our event streaming platform is built on Kafka.
 * Hands-on experience building and operating data pipelines on GCP with BigQuery (and familiarity with multi-cloud patterns).
 * Working knowledge of LakeHouse formats (Iceberg/Delta/Hudi) and columnar storage like Parquet.
 * Experience with infrastructure-as-code and cloud-agnostic tooling (e.g., Terraform, Kubernetes) for deploying and operating data platforms across clouds.
 * Strong in data modeling and optimizing cloud data warehouses - we run on BigQuery and looking to expand to other Cloud DWHs., * Familiarity with observability tools like Prometheus, Grafana, and Loki to monitor and troubleshoot data systems.
 * Exposure to real-time OLAP engines like ClickHouse, Apache Druid, or Apache Pinot for low-latency analytics.
 * Prior experience in fintech or a highly regulated industry, with awareness of data residency, security, and compliance requirements.
 * Experience in Airflow or similar orchestration platforms.

## Description

* Lead the design and build of platform components that run across multiple clouds - addressing portability, cross-cloud replication, HA/DR, and data residency requirements of a regulated fintech environment, and designing against single-cloud lock-in.
 * Build and operate large-scale event streaming infrastructure on Kafka, including configuration tuning and cross-cluster replication between cloud environments.
 * Build and maintain real-time and batch data pipelines using tools like Kafka, Spark, and Airflow.
 * Drive the development of a scalable LakeHouse architecture using modern open table formats such as Iceberg or Delta Lake.
 * Own reliable, cost-aware operation of multi-cloud data infrastructure - SLO-based observability, data quality, and lineage.
 * Enable Analytics and Product teams through self-serve data products, well-modeled warehouse foundations and marts, and reusable ingestion (inbound) and consumption (outbound) patterns.
 * Provide technical leadership to a group of 5-6 engineers and data engineers - leading design reviews, setting engineering standards, and driving knowledge sharing and documentation., * A mentor and multiplier: passionate about driving engineering best practices and guiding a group of engineers to grow and deliver.
 * An owner: you build it, you run it - comfortable carrying operational responsibility for mission-critical streaming and data systems.
 * Comfortable with evolving scope and ambiguity as the platform grows from one cloud to many.
 * Compliance-aware: you treat security, data residency, and auditability as first-class design constraints in fintech.
 * Motivated by aligning data architecture with business outcomes, and by building platforms other teams love to use.