Senior AWS Data Engineer

As a Senior Data Engineer, you will design, build, and continuously improve critical data processing and analytics pipelines. You will solve complex technical problems to enhance platform reliability, resiliency, and scalability, and you will deliver dashboards and reporting solutions that enable stakeholders to analyze performance and make data-informed decisions.

We are seeking a self-directed engineer who thrives with autonomy and can drive long-term projects to completion. You are detail- and quality-oriented, and motivated by the opportunity to deliver measurable business impact through data.

• Translate business requirements into scalable data and analytics architectures with a product-focused mindset.
• Break down complex problems; document technical solutions; and sequence work to deliver fast, iterative improvements.
• Build and scale data infrastructure supporting batch and real-time processing of billions of records.
• Automate cloud infrastructure, services, and observability to improve operational excellence.
• Develop CI/CD pipelines and test automation to improve quality and deployment velocity.
• Partner with data engineers, data scientists, product managers, and other stakeholders to understand needs and promote best practices.
• Identify business challenges and improvement opportunities; apply data analysis and data mining to develop strategic and tactical recommendations.
• Support analytics by delivering insights on product usage, campaign performance, funnel metrics, segmentation, conversion, and revenue growth.
• Produce ad-hoc analyses, long-term projects, reports, and dashboards to uncover insights and measure progress against key initiatives.
• Work closely with business stakeholders to define, track, and maintain focus on critical metrics and KPIs.
• Collaborate cross-functionally to gather requirements and align on delivery.
• Deliver presentations that distill complex problems into clear insights for technical and non-technical audiences.
• Create dashboards that enable stakeholders to interact with data and perform self-serve analysis.

• 4–7 years of relevant industry experience in big data systems, data processing, and SQL databases.
• 3+ years of experience coding with Spark Data Frames, Spark SQL, and PySpark.
• 3+ years of hands‑on programming experience with the ability to write modular, maintainable code (preferably Python and SQL).
• Strong understanding of SQL, dimensional modeling, and analytical data warehouses (e.g., Hive, Snowflake).
• Familiarity with ETL workflow management/orchestration tools (e.g., Airflow).
• 2+ years of experience building reports and dashboards in business intelligence (BI) tools.
• Experience with version control and CI/CD tooling (e.g., Git, Jenkins CI).
• Experience working with and analyzing data using notebook environments (e.g., Jupyter, EMR Notebooks, Apache Zeppelin).

• Knowledge of Looker.
• Experience with additional dashboarding tools such as Qlik and Tableau.

Hive, Spark, Presto, Jenkins, Snowflake, Power BI, Looker, and various AWS services.