Lead Data Engineer - GCP, BigQuery, Python, SQL, Shell Scripting

• This position leads 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 leads and participates in 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 leads others and manages projects within the department and supports Leadership by planning and championing the execution of broad advanced analytics initiatives aimed at delivering value to internal and external stakeholders
• This position contributes to and supports synthesizing disparate data sources to create reusable and reproducible data assets
• This position also supports the data science community working through analytical model feature tuning

RESPONSIBILITIES

• Leads the development and design of the data engineering projects and guides the team to build solutions by leveraging a software/application solution used for statistical modeling and analysis, data warehousing and cloud solutions, and building data pipelines.
• Provides leadership for data engineering projects and data engineering maintenance and support.
• Recommends analytic reporting tools/technologies and leads adoption of emerging technology products and tools.
• Directs integration of data for data engineering projects.
• Leads project efforts with the technology solutions to perform proof of concept (POC) analysis.
• Leads team to provide data warehousing solutions.
• Guides the team to organize the data for reporting, analytics, and data mining.
• Guides the team in building Data APIs to enable data scientists and business intelligence analysts to query the data.
• Conducts code reviews developed in programming language used for statistical analysis and modeling such as Python/Java/Scala/C++.
• Leads the implementation strategy and backup and recovery procedures.

QUALIFICATIONS

• Strong understanding of the data interconnections between organizations operational and business functions.
• Strong understanding of 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.
• Evaluates and provides guidance on analytic reporting technologies (e.g., PBI, Looker, Qlik, etc).
• Strong understanding of a Cloud services platform (e.g., GCP, or AZURE, or AWS) and all the data life cycle stages.
• Understanding of machine learning algorithms which help data scientists make predictions based on current and historical data.
• Knowledge of algorithms and data structures and ability to perform data filtering and data optimization.
• A Bachelor s degree in MIS, mathematics, statistics, or computer science, international equivalent, or equivalent job experience.