Senior Manager - Data Engineering

What You'll Do

1. Lead, mentor, and develop a team of data engineers, fostering a culture of collaboration, innovation, and continuous improvement.
2. Design, build, and maintain scalable data pipelines and architectures for optimal data extraction, transformation, and loading (ETL).
3. Work closely with product managers, analysts, and other stakeholders to ensure data availability and quality for analytical and operational needs.
4. Find opportunities to automate data processes and improve data quality and reliability.
5. Assess and incorporate data management tools, technologies, and standard methodologies.
6. Oversee data engineering projects from conception to delivery, ensuring alignment with business goals and timelines.
7. Ensure data governance, security, and compliance with industry regulations and company policies.

What You Need to Succeed

1. Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
2. Proven experience (15+ years) in data engineering or related roles, with at least 5+ years in a leadership or managerial position.
3. Proficient in writing complex SQL queries across multiple databases (e.g., SQL Server, Azure, MySQL) to efficiently extract, transform, and analyze data.
4. Hands on experience in data engineering tools and technologies such as Python, Databricks, PySparkSQL, Synapse, SSIS, Azure Data Factory, or similar technologies.
5. Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark)
6. Proficient in Microsoft Power Platform, including Power BI, Power Apps, and Power Automate, to deliver integrated data-driven solutions.
7. Demonstrated experience in AI and Machine Learning, with a focus on Generative AI (GenAI) use cases in data engineering, including automating data pipelines and enhancing data quality and analytics.
8. Familiarity with DevOps practices and CI/CD pipelines for data engineering.
9. Demonstrated success in managing large-scale data infrastructure and data pipeline projects.
10. Familiarity with data governance, data quality frameworks, and data security practices.
11. Strong leadership skills with the ability to inspire and guide a team.
12. Strong communication skills to effectively communicate complex data concepts to technical and non-technical collaborators.
13. Strong problem-solving skills and the ability to think strategically about data solutions.
14. A passion for leveraging data to drive business insights and outcomes.