Job Description
Job Overview:
Insight Global is looking for a Senior Data Engineer to join the Data Analytics team at one of our logistics company clients. This person will collaborate closely with multiple stakeholders across the enterprise and Information Technology (IT) group to ensure the most important data is accessible and well-understood.
The Senior Data Engineer designs, develops, implements, and supports new and existing highly efficient ELT/ETL processes and data sets. Other responsibilities include working closely with data consumers, solution architects, security and governance teams to implement solutions to answer complex questions and drive business decisions. Apply your proven communication skills, problem-solving skills, and knowledge of best practices in designing, developing, and deploying data and analytic solutions. Senior Data Engineers need to be adept in several technical and business skills. These include working with diverse datasets, parsing and understanding data, working with domain experts, data scientists and analysts in framing the business problems and provisioning integrated data quickly across multiple environments. Senior Data Engineers need to be inquisitive and motivated to learn modern technologies and capabilities which could benefit the organization and lead the effort in evaluating the technology for acceptance. Senior Data Engineers also assist the business data science efforts with source data, building data sets, helping evaluate models and integrating analytics and data science model outputs into business processes.
Key Responsibilities:
Hybrid cloud environment: the Senior Data Engineer works in a hybrid cloud ecosystem,
composed of Azure, Oracle and on-premises technologies, building and supporting data and
analytics solutions. The Senior Data Engineer will need to learn the data, tools and capabilities
resident in this hybrid ecosystem, such as Synapse, Data Lakes, Dedicated Pool, Azure ML,
SQL Server, SSIS and SSAS.
Build data pipelines: Managed data pipelines consist of a series of stages through which data
flows. Designing, building and maintaining data pipelines, in Azure and the on-premises
ecosystems, will be the primary responsibility of the data engineer.
Drive data centric decision making: Assists with enhancing the data and metadata
management infrastructure to ensure data quality, accessibility and security.
Collaborate across departments: Collaborates with business data consumers, of various skill
levels, in refining their requirements for various data and analytics initiatives. This collaboration
can lead to building enterprise data products, enabling data-driven decision making.
Lead, educate and train: Be curious and knowledgeable about innovative technologies and
data initiatives. Research and propose data ingestion, preparation, integration and
operationalization tools or techniques to aid these initiatives. Train team members, data
consumers, data scientists and data analysts in these technologies and preparation
techniques.
Participate in ensuring compliance and governance during data use: Data engineers work
with data governance teams (and information stewards within these teams) in building, vetting
and promoting content, which adheres to data governance and compliance initiatives.
Become a data and analytics evangelist: The Senior Data Engineer is a blend of
data and analytics "evangelist," "guru" and "fixer." This role promotes the available data and
analytics capabilities and expertise to business unit leaders educating them in leveraging these
capabilities in achieving their business goals.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
A bachelor's in computer science, statistics, applied mathematics, data management,
information systems, information science or a related quantitative field [or equivalent work
experience]
The ideal candidate will have a combination of IT, data governance, analytics, and
communication skills.
At least 5 years or more of work experience in data management disciplines including data
integration, modeling, optimization, data quality and/or other areas directly relevant to data
engineering responsibilities and tasks.
At least 5 years of 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.
Strong experience with various languages and advanced analytics tools such as SQL, Python,
PowerBI, Microsoft SSIS/SSAS, Azure Synapse and others.
Strong ability to design, build and manage data structures and pipelines for encompassing data
transformation, data models, schemas, metadata and workload management. Work with both
IT and business in integrating analytics and data science output into business processes and
workflows.
Strong experience in working with large, heterogeneous datasets in building and optimizing
data pipelines, pipeline architectures and integrated datasets using data integration
technologies, including ETL/ELT, data replication/CDC, message-oriented data movement, API
design and development. - An advanced degree in computer science (MS), statistics, information science (MIS), data
management, information systems, information science (post-graduation diploma or related) or
a related quantitative field [or equivalent work experience]
