Data Modeler

Role: Data Modeler

Location: Philadelphia, PA (onsite)

Job Description:

·
10+ years of experience delivering data and data analytics solutions

· 10+ years of experience with OLTP and OLAP database models

· Azure expertise in end to end (application data base design + ETL /pipeline + Visualization Reporting)

· leveraging Azure technologies such as Azure Data Factory (ADF), Azure Synapse, Azure Purview,

· MS Power BI, Azure DevOps (ADO) etc. is a MUST HAVE

· Must be knowledgeable in software development lifecycles/methodologies i.e., agile

· Data storytelling: Communicate actionable insights using data, often for a non-technical audience.

· Business intuition: Connect with stakeholders to gain a full understanding of the problems they are looking to solve.

· Analytical thinking. Find analytical solutions to abstract business issues.

· Critical thinking: Apply objective analysis of facts before concluding.

· Interpersonal skills: Communicate across a diverse audience across all levels of an organization.

· Has strong presentation and collaboration skills and can communicate all aspects of the job requirements,

· including the creation of formal documentation

· Strong problem solving, time management and organizational skills

· Database Technology: SQL Server, Azure Synapse

· ETL Technology: SSIS, DataStage, Talend, CRON scripting, Perl

· Good to have Cloud SaaS: Talend, Fivetran, HVR, etc.

· Good Understanding in Data Engineering tools, data ops tools in On-prem and Cloud eco system.

· Good to have Azure Certifications

· Understands 3rd party or industry standard file formats (EDI, HL7, NCPDP, etc.)

Responsibilities:

· Responsible for developing effective data architecture solutions (Conceptual, Logical, Physical) that not only satisfy immediate project requirements but also deliver a coherent, reusable, reliable, and phased architecture to help the business grow and change, while developing a long-term data utilization plan for the company’s IT systems and helps architectures through phasing and cost-benefit analysis Demonstrate knowledge of cloud architecture and implementation features (OS, multi-tenancy, virtualization, orchestration, scalability)

· Act as a Subject Matter Expert to the organization for cloud/On-prem end-to-end architecture, should have basic conceptual knowledge of all Cloud platforms like, Azure and future providers, networking, provisioning, and management Communicate and provide thought leadership in Data strategy, technologies, engineering to business and IT leadership teams and stakeholders.

· Providing a vital function bridging the gap between business and technology. In partnership with Product Owner(s), Stakeholders and other Subject Matter Experts, and work with technologies such as Cloud data warehousing, Data lakes, Analytical platforms and solutions.

· Should be able to define modern data eco system, considering current and futuristic data needs Provide advice on technical aspects of development and integration, including the operational and maintenance aspects of systems under development, and proposed system recovery procedures.

· Recommend and ensure that relevant technical strategies, policies, standards, practices, data standards, guidelines, and procedures for design and maintenance are applied correctly

· Support the clients’ enterprise team to respond positively to proposed data initiatives, arising from the Information Governance Group, and provide the necessary support and expertise to deliver such initiatives Provide ongoing support and implementation of change for hydration of data from existing / current systems to future data model

· Apply enterprise data management practices and principles in ways of working and create optimized, reusable semantic model, complete with metadata and lineage information.

· Identify data quality issues through data profiling, analysis and stakeholder engagement

· Analyze data to provide insights for internal and external stakeholders on improving the performance Considers volume, velocity, variety, veracity, performance, exception handling and other factors in the data pipeline

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Thanks and regards