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Contract Duration: Multi-Year - REMOTE Start Date: Immediate Working hours: Mon through Fri, 8 hours/day, 40 hours/week, US Business hours (Central US time zone) *** YOU ARE REQUIRED TO WORK IN US BUSINESS HOURS*** ***YOU MUST UPLOAD YOUR RESUME IN MICROSOFT WORD*** Job Description Our client is looking for a highly skilled Lead Data Engineer with deep expertise in Data Engineering, Python, Snowflake, and DBT (Data Build Tool). Key Responsibilities: - Design and implement scalable data solutions with a strong emphasis on leveraging pattern-based architectural approaches. - Build and manage high-performance data pipelines using tools such as Snowflake, dbt, Kafka, and Airflow, focusing on efficient data ingestion, transformation, and delivery. - Integrate external data sources via APIs (e.g., REST APIs for Facebook, Google Analytics), ensuring smooth and reliable ingestion into the centralized data platform. - Demonstrate expertise in data governance and observability frameworks to ensure data quality, compliance, and security across the data ecosystem. - Develop domain-driven data architectures that align technical solutions with broader business objectives and strategy. - Apply advanced data modeling techniques, including dimensional modeling with a focus on star schemas and other analytics-friendly models. - Collaborate with business stakeholders to understand data requirements and deliver impactful solutions that support key strategic initiatives. - Provide technical leadership and mentorship to the data engineering team, fostering a culture of continuous improvement and innovation. - Proficient in programming languages such as Python, SQL, and Scala, with practical experience in big data frameworks including Hadoop and Spark. Skills - Min 10 years in Data Engineering & Python Programming - Min 8 yrs in Snowflake - Min 8 yrs Scala, SQL - Min 6 yrs in DBT (Data Build Tool), Kafka, Airflow - Strong expertise in the design & build of high-performance data pipelines - Snowflake, Python, and/or other certifications Show more Show less
