What are the key traits of a successful data engineering leader?

Here are my views on the key traits of a successful data engineering leader, from the perspective of technical expertise: →Taking wholistic ownership . →Having solid fundamentals. →Fostering code quality, data quality. →Knowledge of end-to-end architecture. →Striking a balance between tech debts and functional work. →Setting up good monitoring and alerting. →Ensuring the team is engaged with tools and technologies used in the outer world. →Finally being a team sport, not an individual contributor. Listening to the team. "To the team, for the team, by the team". You stand by them as a moral support.

A successful data engineer possesses strong technical skills in languages like Python and SQL, adept database management capabilities, and proficiency in tools like Spark and Hadoop. They apply these skills to build efficient data pipelines, ensuring accurate data analysis. Additionally, they prioritize effective communication, collaborating across teams and conveying complex technical concepts to non-technical stakeholders. Their commitment to data security and continuous learning reflects their adaptability in an ever-evolving tech landscape.

Having a solid understanding of the architecture is key for the success of the Data engineering leader. It is possible that sometimes the leader has to roll up their sleeves to get down to the weeds. A strong technical background will come to help. After-all, the leader has to be a team player themselves when the team needs help.

Data Generals: Leading the Charge in 6 Powerful 1. Tech Jedi: Mastering the data labyrinth, from architecture to code, they guide their teams like Obi-Wan Kenobis (minus the beard). 2. Business Whisperer: Bridging the gap between tech jargon and boardroom brilliance, they speak the language of both data and dollars. 3. Team Architect: Building high-performing squads is their forte. They inspire, coach, and delegate, forming a collaborative data fortress. 4. Communication Maestro: Whether explaining AI to the CEO or rallying the troops, their words resonate, leaving a trail of clarity and action.

A leader, who lead and have the insight of the basic data engineering principles and who understands the data and can drive and pass the technical as well as business goal to its subordinates in a very layman’s language. This could be achieved when a leader is technically strong, understands the design complexity as well as developers challenge. If a leader is technical expert, team will deliver high performing solutions with minimal tech debt.

Business Acumen: Understands the business objectives and can align data engineering initiatives with overall business goals. This involves identifying opportunities where data can drive business value. Roadmap Planning: Develops and communicates a clear roadmap for data engineering projects, considering both short-term goals and long-term vision. Adaptability: Can adapt strategies and priorities based on evolving business needs and technological advancements.

This is such an important aspect. We first need to have a solid understanding of the impactful business problems we are looking to solve and then figure out a solution using technology and tools. If we first pick the technology and then figure out how it can help solve business problems, we may end up working on the least impactful business goals.

Curiosity is very important especially in this field. Asking more questions helps in ensuring the project outcome aligns with the business direction and goals.

A data engineering leader should not only understand the technical side of data engineering, but also the business side. They should be able to align the data engineering goals and priorities with the business objectives and needs, and demonstrate the value and impact of the data products to the organization. A data engineering leader should also be able to identify and anticipate the business requirements and opportunities for data engineering, and translate them into actionable and feasible plans and roadmaps.

This is one of my favorites. Understanding the strengths of every member in the team is a big need for the data engineering leader. If we know which team member or team members should bring working on an initiative by complementing their strengths , then half the problem is solved. Knowing the team at personal level also builds trust. This is another big aspect of leadership.

A successful data Engineering leader should be aware about business and his team strength. At leadership stage, you are expected to have sound understanding of business and workable understanding of high level tools and technologies. You are not supposed to have hard core handa on exp but knowing the architecture, frameworks, differ data flow approaches will help you to wrap up the things is better way. Last but not the least, I believe the most important skills every leader should have are good communication and engagement skills. Your leadership skills must have client engagement and team integration qualities.

Team Building: Capable of building and leading a high-performing data engineering team. This includes hiring the right talent, fostering collaboration, and creating a positive work environment. Communication: Effectively communicates complex technical concepts to both technical and non-technical stakeholders. This includes presenting data strategies, project updates, and insights derived from data. Empowerment: Encourages and empowers team members to take ownership of their work, fostering a sense of responsibility and accountability.

A data engineering leader should be able to manage and motivate the data engineering team, which may consist of different roles, skills, and backgrounds. They should be able to define and delegate the roles and responsibilities, provide feedback and guidance, and foster a culture of trust and accountability. A data engineering leader should also be able to recruit, retain, and develop the data engineering talent, and create a learning environment that encourages continuous improvement and innovation.

A successful data engineer stands out with technical prowess in Python, SQL, database management, Spark, and Hadoop, leveraging these tools to construct efficient data pipelines. Their standout trait is not only technical acumen but also strong communication skills. They excel in conveying complex technical insights to diverse teams, fostering collaboration, and ensuring a shared understanding of data-related concepts among both technical and non-technical stakeholders. This ability to bridge the communication gap empowers them to contribute effectively to data-driven decision-making processes within organizations.

Perhaps in the case of the Data Science team, it is not only about communication but a solid collaboration in every aspect of the company. When companies shift towards a data-driven approach, the data engineering and data science team also shifts from a secondary role into a primary one, and if both teams do not work closely together, the rest of the company may start questioning the value added by data and people might attempt to switch back to old processes.

Effectively communicates complex technical concepts to both technical and non-technical stakeholders. This includes presenting data strategies, project updates, and insights derived from data.

Analytical Thinking: Possesses strong analytical skills to identify and solve complex data-related problems. Innovation: Encourages a culture of innovation within the team, exploring new technologies and methodologies to improve data engineering processes.

Every project has it's own unique challenges, whether it is from the technical aspect or the business aspect. Being able to navigate these challenges and propose solutions is a critical skill.

A data engineering leader should be able to solve complex and challenging problems that may arise in the data engineering projects, such as data quality issues, data integration challenges, data security risks, or performance bottlenecks. They should be able to analyze and diagnose the root causes of the problems, explore and evaluate different options and alternatives, and implement and test the best solutions. A data engineering leader should also be able to learn from the failures and mistakes, and prevent or mitigate the recurrence of the problems.

Every Data Engineering Manager needs to be thinking at least 2-3 years ahead of their time. If they are not able to do it, their teams will be short or even out of work in the future. It is a rapidly changing field with new interpretations/additions coming almost every quarter. Any DE Manager failing to upskill and more importantly, reskill their team, will fail to bring in work and eventually lose the team. This is a losing scenario for both the Manager and the team and only winners are those who foresee how to change and adapt.

Visionary does not necessarily mean cutting edge. It's about setting a course, defining a strategy, a roadmap and driving toward this goal. You need to keep in mind, that to bring people along on your journey, you need to: 1) When figure heading a vision, what is the end state? If isn't data or data engineering. It's the result that you want people to subscribe to 2) People tend to be siloed in their thinking, and even fear change, so you need to demonstrate the bridge that will take them from where they are now to where your vision will take them 3) Find people in your network who share, or demonstrate an alignment with your vision. You need these people Your vision can be anything, tech, new way of working, or thinking, anything

A successful data engineering leader should have a strategic vision aligned with business goals, analytical and innovative thinking, effective time and resource management skills, an active listener with effective communication skills, a good understanding and implementation of ethical considerations, adaptability, etc.

You need to learn to: 1) Delegate 2) Trust your team 3) Provide psychological safety for your team. They need to know that you have their back 4) Encouragement. A key part of your role is to tell people how well they are doing 5) Be honest with those that work for you 6) Be able to accept feedback from those that work for you 7) It's not good enough to tell those that work for you, that you have an open door policy, you need to proactively go to them 8) Appreciate that one size does not fit all. You need to support both junior and senior colleagues, and each has their own needs 9) Do not be afraid of firing people 10) Your job is to develop your team. They will leave your team at some point and this is okay

A good data engineering leader should also be well placed with good networking in the software vendor space. This would allow better access to technologies, better positioned to make comparative assessments and to bring in external help to assist internal team members or external stakeholders.

"Data is the new oil" a common term among businesses. Still, data strategy and tools are usually overlooked in many enterprises, especially those whose main line of business is outside technology. More than a data engineer leader, enterprises need a person who understands the requirements and technology and has the power to shape and make decisions regarding data. Traditional companies emphasize the role of CEOs, CFOs, and CTOs, putting them on the top of the command chain, but modern enterprises need to figure out the position and power of a new type of leader, the CDO (Chief Data Officer). If companies want to compete and thrive in the modern AI world, it is time to start listening to your data experts and engineers.