We have an opening for a Data Engineering Lead. The Data Engineering Lead is responsible for leading the expansion and optimization of the firm's data and data pipeline architecture as well as data flow and collection. They ensure the integrity of all data from source systems into the data warehouse, as well as all data provided to our ETL processes. This individual directs and mentors a small team of data analytics engineers and ETL developers in support of these efforts. In this capacity, the Data Engineering Lead will:
- Define and create data architectures which supports the requirements of the business;
- Develop custom data models and algorithms to apply to data sets;
- Assess the effectiveness and accuracy of new data sources and data gathering techniques Client opportunities for data acquisition;
- Develop data set processes for data modeling, mining and production;
- Oversee the data team and in collaboration with IT leadership provide day to day guidance and direction to achieve organizational goals in the timelines set;
- Employ a variety of languages and tools to marry systems together;
- Recommend ways to improve data reliability, efficiency and quality;
- Leverage large volumes of data from internal and external sources to answer business demands;
- Introduce automation through effective metadata management and using innovative and modern tools and techniques. Partially or completely automate the most common and repeatable data preparation and integration tasks;
- Propose appropriate data ingestion, preparation, integration and operationalization techniques in addressing data requirements;
- Lead the development of data governance policies and best practices for consumers and users of data we provision;
- Coordinate with different functional teams to implement models and monitor outcomes;
- Develop processes and tools to monitor and analyze model performance and data accuracy.
Proficiencies:
- Experienced in designing, building and managing data pipelines for data structures;
- Expertise with advanced analytics tools for Object-oriented/object function scripting. Includes languages such as C#, Python and others;
- Expert in SQL, PL/SQL, SSIS and SSAS;
- Knowledge and/or certifications on upcoming NoSQL/Hadoop-oriented databases like MongoDB, Cassandra, and others for non-relational databases;
- Strong experience in working with large, heterogeneous data sets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies;
- Experienced working with popular data discovery, analytics, and BI software tools such as Tableau, Power BI and others for semantic-layer-based data discovery;
- Experienced working with data governance/data quality and data security teams;
- Experienced employing MicroSoft MDM (Master Data Management) and MDS;
- Ability to troubleshoot complicated issues across multiple systems and driving solutions;
- Effectively convey technical concepts to non-technical individuals;
- Demonstrate a high level of Data Security Awareness;
- Financial data and or experience with the legal industry data is a plus;
- Experienced performing ETL using Alteryx/Intapp Integrate/Boomi is a plus.
Qualifications:
- A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience;
- At least 8 years' experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks;
- At least 5 years' experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative;
- Excellent verbal and written communication skills;
- Excellent problem solving and analytical skills;
- Must be highly effective within a collaborative environment;
- Must be able to independently resolve issues and efficiently self-direct work activities based on the ability to capture, organize, and analyze information