Data Engineer

📋 Description

• • Own the end-to-end life-cycle of ETL data pipelines and integrations that power critical business decisions across Fhiworks. You will be the first line of defense for production incidents, ensuring 24/7 reliability of data flows that feed Power BI, SSRS, and Workday dashboards used by finance, HR, and operations teams.
• • Serve as Level-1 production support for all data-flow incidents, triaging alerts, diagnosing root causes, and implementing fixes within agreed SLAs. You will monitor job schedules, optimize runtimes, and maintain integration catalogs so stakeholders always know the status and lineage of their data.
• • Translate complex upstream and downstream changes into seamless end-to-end reporting solutions. When source systems evolve, you will assess impact, refactor pipelines, and validate that downstream reports remain accurate—protecting business continuity and user trust.
• • Build and refine scalable data pipelines that ingest, cleanse, and transform large datasets. You will apply best-practice data architecture and modeling techniques—both normalized and denormalized, star and snowflake—to ensure datasets meet functional business needs and non-functional requirements such as performance, security, and maintainability.
• • Automate repetitive manual processes using Python, Pandas, and containerized scripts. From error handling and logging to loop-based batch processing, your code will reduce manual touchpoints, accelerate data delivery, and improve overall reliability.
• • Create crystal-clear documentation for every ETL process, object usage pattern, and data model. You will also author and execute test plans to validate code changes, ensuring that every deployment is traceable and reversible.
• • Partner closely with BI analysts and business stakeholders to understand reporting requirements, translate them into technical specifications, and deliver datasets that enable self-service analytics. Your ability to communicate complex technical concepts in plain language will be key to adoption.
• • Continuously improve data quality by profiling source data, implementing validation rules, and building automated quality checks. You will track DQ metrics and present findings to leadership, driving a culture of data excellence.
• • Contribute to the SDLC by participating in sprint planning, code reviews, and retrospectives. You will use Git for version control, follow branching strategies, and ensure every change is peer-reviewed and tested before reaching production.
• • Stay current with emerging technologies and evaluate their fit within our stack. Whether it’s a new Azure service, an AWS analytics tool, or an advanced Snowflake feature, you will prototype, benchmark, and recommend adoption when it adds clear value.
• • Work fully remote while aligning your schedule to Eastern Time. Although you will collaborate with distributed teams daily, we have a strong preference for candidates located in North Carolina to facilitate occasional in-person collaboration and knowledge-sharing sessions.