Data Engineering Lead (Charlotte)

Mid / Senior



Meytier Premier Employer

Working there

About This Workplace

Meytier Partner

As a Data Engineering Consultant, you will apply strong expertise in AI through the use of data engineering, machine learning, data mining, and information retrieval to design, prototype, build and deploy next generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.


  • 10+ years of experience in implementing and managing high performance scalable enterprise applications in the Financial Services industry.
  • Extensive experience on the Hadoop platform and related Big Data tools/technologies.
  • Good knowledge of architecture, design patterns, Source target mappings, ETL Architecture in Hadoop space, data modeling techniques, performance tuning in Hadoop environment.
  • Responsible for helping team turn data into knowledge to help them make better decision, faster.
  • Work with clients and team members to analyze and help define requirements, mine and analyze data, integrate data from a variety of sources, and participate in the design and implementation of reports, algorithms, and other data processing and analysis techniques.
  • Deliver high-quality data pipelines for producing analytics-ready datasets.
  • Responsible for delivering end-to-end analytics projects including, data: ingest, transportation, silence and visualization.
  • Design and deploy databases(Tenants) and data pipelines to support analytics projects.
  • Clearly document datasets, solutions, findings, and recommendations to be shared internally and externally.
  • Apply technologies proficiently including: SQL/Hive, Python, PySpark, Spark/Mapreduce, Bash, Hadoop, Azure, Oozie WF, Data science Anaconda Enterprise, Jupyter, Tableau.
  • Complete performance optimization for queries and dashboards, and develop and deliver clear, compelling briefings to internal and external stakeholders on his findings, recommendations and solutions.
  • Analyze client data and systems to determine whether requirements can be met.
  • Test and validate data pipelines transformations, datasets, reports and dashboards built by the team.
  • Develop and communicate solutions architectures and present solutions to both business and technical stakeholders.
  • Provide end user support to other data engineers and analysts.
  • Build compelling visualizations and dashboards that address the analytics needs of the end-user or customer.

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