- Utilize consulting and technical skills to be able to work in a client-facing project environment independently.
- Be responsible for your own execution and often others' on client projects, communicating directly with internal and external stakeholders on status updates and potential roadblocks.
- Collaborate with other team members to successfully deliver on projects.
- Work effectively and directly communicate with both internal and client and/or partner teams.
- Develop full ownership of your execution on client engagements, play a role in the project planning and solution stages of engagements as well.
- Lead the end-to-end implementation of multiple ETL/ELT pipelines, demonstrating efficient data transformation.
- Mentor junior data engineers, and their growth is evident in their project contributions
- Successfully lead small data warehousing projects with measurable performance enhancements under the management of an engagement lead- may also play the role of an engagement lead.
- Contribute to real-time data processing solutions and managed streaming data.
- Implement security and compliance measures for data pipelines.
- Design and implement version control and branching strategies and integrate them into CI/CD for promoting and testing in higher environments.
- B.S. in Computer Science or equivalent
- 4-6 years' experience in data engineering and big data. 2 years' of professional services experience interacting directly with clients.
- Extensive knowledge of data warehousing concepts and hands-on experience deploying pipelines using Databricks **a must
- Databricks Solution Architect certification a plus.
- Data modeling and database design skills and knowledge of version control
- Excellent verbal and written communication skills
- Experience architecting scalable and fault-tolerant data solutions across Azure, AWS, and Databricks
- Understands and utilizes Lovelytics tools and client tools
- Exciting projects with great clients in varying departments and verticals across the world
- The ability to work closely with experienced data engineers and quickly grow and expand your skillset
- The ability to work closely with all sizes of companies, ranging from Fortune 100 to small local businesses
- A workplace where you are encouraged to challenge the status quo and develop new technologies, methodologies, and processes
- A diverse team consisting of data gurus, experience seekers, and entrepreneurial minds that are always pushing to be better
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Tech Lead, Data Engineer - Arlington, United States - Lovelytics
Description
Lovelytics is seeking a Technical Lead (Level 4) Consultant with experience delivering strategic Databricks client engagements to join our Data & AI practice
As a Tech Lead, you will gain people management skills in order to develop to the next level. In addition, you will play a key role, often as an engagement lead, on client engagements related to data warehousing, ETL development, data integrations, and data modeling. This is a client-facing and stakeholder management role, focused on using and migrating to our partner technologies; Databricks, AWS, and Azure to name a few. In addition to the technical capabilities for this role, we are looking for someone who wants to work in a collaborative, dynamic, and inclusive environment and has a passion for bringing meaning to data.
Role Location: Open to remote candidates in the US in the following states: MD, DC, CA, IL, IA, IN, MA, NC, TX, TN, GA, CO, NY, NJ, VA, FL, PA, OH, OR. As well as Ontario, Canada
This role is not eligible for sponsorship at this time.
Primary Responsibilities:
Our Ideal Candidate's Skills and Experiences:
What We Promise You:
Lovelytics is an Equal Opportunity Employer. This means you don't have to worry about whether your application process will be fair. We consider all applicants without regard to race, color, religion, age, ancestry, ethnicity, gender, gender identity, gender expression, sexual orientation, veteran status, or disability. US Salary for this position is $125,000-160,000, however, actual salary is based on a number of various factors including skillset, experience, credentials, etc.