Senior Data Engineer

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SENIOR DATA ENGINEER JOB SUMMARY This position supervises and participates in the development of batch and real-time data pipelines utilizing various data analytics processing frameworks in support of data science and machine learning practices. This position participates in and supports the integration of data from various data sources, both internal and external. This position performs extract, transform, load (ETL) data conversions, and facilitates data cleansing and enrichment. This position performs full systems life cycle management activities, such as analysis, technical requirements, design, coding, testing, implementation of systems and applications software. This position participates and contributes to synthesizing disparate data sources to create reusable and reproducible data assets. This position contributes to the data science community working through analytical model feature tuning.

Supervises and supports data engineering projects and builds solutions by leveraging a strong foundational knowledge in software/application development.  Develops and delivers data engineering documentation.  Gathers requirements, defines the scope, and performs the integration of data for data engineering projects.  Recommends analytic reporting products/tools and supports the adoption of emerging technology. Performs data engineering maintenance and support. Provides the implementation strategy and executes backup, recovery, and technology solutions to perform proof of concept (POC) analysis. Performs ETL tool capabilities with the ability to pull data from various sources and perform a load of the transformed data into a database or business intelligence platform. Builds data APIs to enable data scientists and business intelligence analysts to query the data. Codes using programming language used for statistical analysis and modeling such as Python/Java/Scala/C++.

Literate in the programming languages used for statistical modeling and analysis, data warehousing and Cloud solutions, and building data pipelines.  Strong understanding of database systems and data warehousing solutions. Strong understanding of the data interconnections between organizations’ operational and business functions. Strong understanding of the data life cycle stages - data collection, transformation, analysis, storing the data securely, providing data accessibility  Strong understanding of the data environment to ensure that it can scale for the following demands: Throughput of data, increasing data pipeline throughput, analyzing large amounts of data, Real-time predictions, insights and customer feedback, data security, data regulations, and compliance. Strong knowledge of algorithms and data structures, as well as data filtering and data optimization. Strong understanding of analytic reporting technologies and environments (e.g., PBI,.)  Strong understanding of a cloud services platform (e.g., GCP, or AZURE, or AWS) and all the data life cycle stages. Proficiency in Databricks notebooks using Python, SQL, and PySpark for data transformation, modeling, and analysis. Strong communication and storytelling skills to present complex data concepts in clear, non‑technical language. Experience working with cross‑functional teams (product, operations, finance, marketing, compliance). Understanding of business KPIs, metrics, and