Manager, Data Engineering

infrastructure meets business needs and is scalable, reliable, and efficient.

Key Responsibilities:

1. Team Leadership:

• Manage, mentor, and guide a team of data engineers, ensuring their professional development and optimizing team performance.
• Foster a culture of collaboration, accountability, and continuous learning within the team.
• Lead performance reviews, provide career guidance, and handle resource planning.

Data Engineering Analytics:

• Design and implement data pipelines, data models, and architectures that are robust, scalable, and efficient.
• Develop and enforce data quality frameworks to ensure accuracy, consistency, and reliability of data assets.
• Establish and maintain data lineage processes to track the flow and transformation of data across systems.
• Ensure the design and maintenance of robust data warehousing solutions to support analytics and reporting needs.

Collaboration and Stakeholder Management:

• Collaborate with stakeholders, including data scientists, analysts, product managers, engineering managers, and business leaders, to understand business needs and translate them into technical requirements.
• Work closely with these stakeholders to ensure the data infrastructure supports organizational goals and provides reliable data for business decisions.

Project Management:

• Drive end-to-end delivery of analytics projects, ensuring quality and timeliness.
• Manage project roadmaps, prioritize tasks, and allocate resources effectively.
• Manage project timelines and mitigate risks to ensure the timely delivery of high-quality data engineering projects.

Technology and Infrastructure:

• Evaluate and implement new tools, technologies, and best practices to improve the efficiency of data engineering processes.
• Oversee the design, development, and maintenance of data pipelines, ensuring that data is collected, cleaned, and stored efficiently.
• Ensure there are no data pipeline leaks and monitor production pipelines to maintain their integrity.
• Familiarity with reporting tools such as Superset and Tableau is beneficial for creating intuitive data visualizations and reports.

Machine Learning and GenAI Integration:

• Machine Learning:

  Knowledge of machine learning concepts and integration with data pipelines is a plus. This includes understanding how machine learning models can be used to enhance data quality, predict data trends, and automate decision-making processes.

• GenAI:

  Familiarity with Generative AI (GenAI) concepts and exposure is advantageous, particularly in enabling GenAI features on new datasets. Leveraging GenAI with data pipelines to automate tasks, streamline workflows, and uncover deeper insights is beneficial.

Qualifications and Skills:

Experience:

• 10+ years of experience in data engineering, with at least 2 years in a managerial role.
• Experience in any SaaS company is highly beneficial, as is an understanding of common SaaS business processes, including familiarity with metrics such as customer acquisition costs, retention rates, and revenue growth.

• Technical Expertise:

  Strong knowledge of data engineering concepts, including data warehousing, ETL processes, and data pipeline design. Proficiency in SQL, Python, and other data engineering tools.

• Data Modeling:

  Expertise in data modeling is essential, with the ability to design and implement robust, scalable data models that support complex analytics and reporting needs. Experience with data modeling frameworks and tools is highly valued.

• Leadership Skills:

  Proven ability to lead and motivate a team of engineers while managing cross-functional collaborations.

• Problem-Solving:

  Strong analytical and troubleshooting skills to address complex data-related challenge.

• Communication:

  Excellent verbal and written communication skills to effectively interact with technical and non-technical stakeholders. This includes the ability to motivate team members, provide regular constructive feedback, and facilitate open communication channels to ensure team alignment and success.

• Data Architecture:

  Experience with designing scalable, high-performance data systems and understanding cloud platforms such as AWS, Google Cloud, Data Bricks, Click house, Snowflake, or Azure.

• Machine Learning and GenAI:

  Knowledge of machine learning concepts and integration with data pipelines, as well as familiarity with GenAI, is a plus.

• Data Governance:

  Experience with data governance best practices is desirable.

• Open Mindset:

  An open mindset with a willingness to learn new technologies, processes, and methodologies is essential. The ability to adapt quickly to evolving data engineering landscapes and embrace innovative solutions is highly valued.