Manager, Data Engineering and Operations
3/16/2026
This role involves building, enhancing, and optimizing ETL jobs, implementing automated data warehouse solutions, and conducting QA while providing support and mentorship to other team members. Key tasks include gaining complete oversight of the data warehouse and collaborating with stakeholders on investment, strategy, and analytical teams.
Salary
70000 - 110000 USD
Working Hours
40 hours/week
Company Size
1,001-5,000 employees
Language
English
Visa Sponsorship
No
The Company:
Hearts & Science has been inspired by confident marketers seeking business advantage in a world of personalized digital marketing, where CRM and addressable channels converge, and decisions must be made in real time to aggregate effective reach and deliver the right message at the right time.
Designed to inform brand strategies with real-time insights, Hearts & Science is a data-driven marketing agency with expert media planning and buying capabilities, among other services that include shopper marketing, marketing innovation and content activation.
Role Overview
This role will be reporting to the Senior Director, Data Engineering and be part of a Data Engineering Team who build, enhance and optimize ETL (Extract, Transform, Load) jobs, implement automated data warehouse solutions, conduct QA and manage day-to-day tasks. The candidate must have strong data warehouse, data pipeline design/development, SQL, AWS Cloud and Big Data Technologies experience. As a Manager, Data Engineering & Operations, you will also provide support and mentor other team members who will look for your experience and guidance to solve a problem. Complete oversight of our data warehouse and the ability to work directly with stakeholders on Investment, Strategy and Analytical teams are additional tasks that will be expected of you.
You will have the opportunity to contribute to end-to-end platform design for our cloud architecture and work multi-functionally with our Analytics, Data Science, IT and Investment teams to build batch and real-time data solutions. This is a very hands-on position, requiring extensive experience developing workflows in Python, willingness to dive into legacy code repositories and maintain complex data transformation processes on AWS cloud. Good decision-making, time management, prioritization and escalation skills are traits needed to ensure continued success in this role.
Qualifications
Education & Technical Experience Requirements
- Bachelor’s in Computer Science or similar technical degree within a STEM field
- 3+ years of hands-on experience in building data pipelines using traditional ETL tech stack, platform APIs and big data technologies
- 3+ years of experience in programming languages including as Python and working within a Linux based environment
- Experience designing, deploying and supporting production cloud services in AWS ecosystem including EC2, S3 and Redshift
- Can speak to concepts and designs/benefits to multiple audiences as well as interpret high level business requirements from conversation
- Able to work within a cross-functional team environment with people from multiple business units, vendors, countries, and cultures
- Working experience within the advertising/media industry with knowledge of basic media math
- Experience in Social Media Datasets such as Twitter, Facebook, Instagram is a plus
- Experience in Google Campaign Manager, Xandr, or Adobe Analytics datasets is a plus
- Experience with Google Cloud products is a plus
- Experience with Microsoft PowerAutomate is a plus
- Experience using Project Management tools such as Jira is a plus
- Experience creating data visualizations and using tools such as Tableau is a plus
Key Responsibilities:
- Contribute to the growth of the enterprise data warehouse (EDW) by working alongside stakeholders to produce accurate information within database objects used in reporting, strategic planning and decision making
- Assist the Senior Director in developing long-term strategies and capacity planning for meeting future data warehouse and data automation needs
- Meet with end-users and data SMEs to clarify requirements and business rules needed to design, develop and implement ETL/ELT processes
- Collaborate with peer Data Engineers to build upon and support our CI/CD pipelines hosted within our extensive GIT repository
- Mentor our emerging Data Analysts, including providing feedback and suggestions for their planned workflows prior to deployment into production
- Respond, investigate and correct reported data inaccuracies within the EDW compared to source and business-expected results
- Establish relationships with external and internal POCs to ensure seamless inbound and outbound Data Delivery expectations are met
- Implement and support backend maintenance processes that enforce ongoing data optimization and corporate retention policies while ensuring the historical data needs of the client are met
- Document and maintain all the processes and procedures pertaining to ETL and data warehouse usage/development, contributing to the team knowledgebase when applicable
#LI-CC2
This is the pay range the Company believes it will pay for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on relevant experience, other job-related qualifications/skills, and geographic location (to account for comparative cost of living). The Company reserves the right to modify this pay range at any time. For this role, benefits include: health insurance, vision insurance, dental insurance, 401(k), Healthcare Flexible Spending Account, Dependent Care Flexible Spending Account, vacation days, sick days, personal days, paid parental leave, paid medical leave, and STD/LTD insurance benefits.
This role is hybrid, requiring three (3) days per week in the office. The remaining two (2) days may be worked remotely. Specific in-office days will be discussed during the interview process, with flexibility to align with team needs. Please note that the number or required in-office days may be adjusted over time, potentially increasing the number of required in-office days based on business needs.
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