Data Engineering Lead

Team Leadership and Management:

• Team Supervision: Lead and mentor a team of data engineers to ensure high performance, professional growth, and alignment with organizational goals.
• Task Allocation: Assign tasks and manage workloads effectively across the team, ensuring timely delivery of projects while maintaining quality.
• Collaboration: Work closely with cross-functional teams (e.g., data scientists, analysts, business stakeholders) to ensure the data infrastructure supports their needs and objectives.
• Training and Development: Provide guidance, training, and upskilling opportunities to junior and mid-level data engineers.

Data Architecture Design:

• Pipeline Development: Design and implement scalable, reliable, and efficient data pipelines to process and transform large datasets from various sources.
• Architecture Strategy: Lead the design and architecture of data systems and solutions, ensuring data is easily accessible, clean, and properly formatted for analytics.
• Data Modeling: Work on data models, including data warehouses, lakes, and other data storage solutions, and design the schema that supports efficient querying and reporting.

Data Integration and ETL:

• ETL Processes: Lead the development of Extract, Transform, Load (ETL) processes to integrate data from multiple sources, ensuring data quality, consistency, and reliability.
• Data Quality Assurance: Ensure that the data pipelines and processes meet high standards of data accuracy, consistency, and timeliness. Establish data validation processes and error handling mechanisms.

Data Warehousing and Storage:

• Data Storage Solutions: Lead efforts in selecting and implementing data storage solutions, such as data lakes, data warehouses, and cloud storage platforms (e.g., Amazon Redshift, Google BigQuery, Snowflake).
• Scalability and Performance: Design and optimize data storage systems for performance, scalability, and cost-efficiency, taking into account future growth and business requirements.
• Database Management: Oversee the management of relational and non-relational databases, ensuring optimal performance and security.

Technical Expertise:

• Expertise in data engineering concepts, such as ETL, data warehousing, data lakes, and data pipelines.
• Proficiency in programming languages such as Python, Java, Scala, SQL, and frameworks like Apache Spark or Apache Flink for big data processing.
• Strong experience with cloud platforms (AWS, Google Cloud, Azure) and associated services like S3, Redshift, BigQuery, EMR, and Databricks.
• Familiarity with containerization tools (e.g., Docker, Kubernetes) for orchestrating and deploying data pipelines.

Database and Storage Knowledge:

• Deep knowledge of relational and NoSQL databases, including MySQL, PostgreSQL, MongoDB, Cassandra, and HBase.
• Familiarity with data storage solutions, including Data Lakes (e.g., AWS S3, Google Cloud Storage), Data Warehouses (e.g., Snowflake, BigQuery), and distributed file systems like HDFS.

Leadership and Management:

• Experience managing and leading a team of data engineers, with strong organizational, mentoring, and communication skills.
• Ability to manage multiple projects simultaneously, ensuring deadlines are met without compromising quality.
• Conflict resolution, team-building, and performance management skills.

Data Modeling and Architecture:

• Expertise in data modeling techniques and building scalable data architectures.
• Ability to design efficient data models that support analytics, business intelligence (BI), and machine learning (ML) use cases.

Problem Solving and Optimization:

• Strong analytical and problem-solving skills to optimize data pipelines and ensure efficient data processing and storage.
• Experience in debugging and troubleshooting complex data issues.

Soft Skills:

• Excellent communication skills to interact with both technical and non-technical stakeholders.
• Strategic thinking to understand business needs and align technical solutions with those needs.
• Ability to work in a fast-paced, collaborative environment and manage competing priorities.

Agile Methodologies:

• Familiarity with agile methodologies (e.g., Scrum, Kanban) for managing projects and ensuring iterative delivery of data solutions.