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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Jobot - **Location:** United States - **Experience:** Expert - **Salary:** $75,000.0 - $115,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Cloud Database, Databases, Information Engineering, Data Infrastructure, Data Systems, Data Warehousing, JSON, Python (Programming Language), CURL, Scrum Methodology, Data Streaming, Tableau (Software), Technical Data Management Systems, Usage Analysis, Parquet, Sql Optimization, DevOps Tools - Open-source, Snowflake, Data Layers, Kubernetes, Information Technology, Data Analytics, Performance Monitor, Apache Kafka, Build Tools, Api Design, Terraform, Looker Analytics, Docker - **Published:** August 29, 2026 - **Apply:** https://www.dice.com/job-detail/05aaabd8-34a1-4bce-99dc-2c1bddba7b56 ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field * 7+ years of professional experience in data or analytics engineering * Proven ability to translate business needs into technical data solutions in fast-paced, cross-functional environments * Expertise in relational, dimensional, and semantic data modeling * Hands-on experience designing, implementing, and maintaining modern data warehouses (Snowflake preferred) * Advanced SQL skills, including optimization and complex transformations * Experience with DBT for modular, testable transformations * Proficiency with orchestration and ingestion tools such as Airflow, Prefect, Airbyte, Fivetran, or Kafka * Familiar with ELT, schema-on-read, and DAG design principles * Strong experience with AWS (S3, RDS, Redshift, etc.) * Familiarity with Terraform, Docker, and containerized workflows a plus * Skilled in handling structured, semi-structured (JSON), and columnar data formats (Parquet, ORC) * Experience building and supporting semantic layers for self-serve analytics * Proficient with BI tools such as Looker, Tableau, or Sisense * Proficient in Python and Unix/Linux scripting * Comfortable working with APIs and automation tools (e.g., curl) Bonus Points * Experience with real-time streaming (Kafka, AWS Kinesis) * Familiarity with DevOps tooling (Terraform, Kubernetes, Docker) * Experience in Agile (Scrum or Kanban) environments with iterative sprint-based delivery * Strong communication and change management skills in data-driven organizations ## Description We're seeking a Senior Data Engineer to take ownership of the next evolution of our data ecosystem-from ingestion pipelines to analytics platforms. This role sits at the intersection of technology, strategy, and insight, empowering better decisions across the business through scalable data infrastructure and intuitive access to information. The ideal candidate will build and optimize the pipelines, frameworks, and architecture that drive internal analytics, financial reporting, and product intelligence. Your work will directly influence how teams across the organization-from Product to Analytics-leverage data for performance, prediction, and growth. Key Responsibilities * Collaborate with Product, Analytics, and Engineering to design and implement scalable data systems that unlock value from diverse sources including databases, event streams, and external platforms * Define and drive the technical roadmap for data engineering with both immediate and long-term scalability in mind * Standardize metrics and data definitions to align teams across the organization * Enable internal use cases such as financial forecasting, operational reporting, and product analytics * Enable external data products such as dashboards, self-serve analytics, and in-product intelligence * Manage the complete data lifecycle-from ingestion and transformation to consumption and visualization * Build tools and frameworks that enhance transparency and trust in business performance reporting * Deploy and maintain robust, cloud-based data infrastructure (AWS preferred) * Establish and enforce frameworks for data quality, governance, and security * Champion best practices in data engineering and continuous improvement ## Related Videos - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Don’t Insert Crazy! 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