Data Engineering Manager

Data Engineering Manager

 

*Must be very technical and hands-on with SQL, python, Snowflake, AWS, ETL, ELT, DevOps best practices and Site Reliability*

 

We are seeking an experienced Data Engineering Manager with deep expertise in architecting and building scalable data warehouses on AWS and Snowflake. In this role, you will lead a team of data engineers to design, implement, and maintain robust data pipelines and ETL processes that empower data-driven decision-making across the organization.

Key Responsibilities

Leadership & Strategy

• Team Leadership: Recruit, mentor, and develop a high-performing team of data engineers, fostering a culture of collaboration, innovation, and accountability.
• Strategic Vision: Develop and execute a data engineering roadmap that aligns with overall business objectives and drives data-driven insights.
• Stakeholder Collaboration: Work closely with analytics, business intelligence, and other key stakeholders to understand data requirements and deliver effective data solutions.
• Best Practices: Establish and promote best practices in data architecture, warehousing, and software development.

Data Engineering & Warehousing

• Data Warehouse Architecture: Design and implement scalable data warehouses on AWS, leveraging Snowflake as the primary solution.
• Pipeline Development: Build and maintain robust, end-to-end data pipelines and ETL processes to ensure efficient, accurate, and secure data flow.
• Data Quality & Security: Ensure high standards of data quality, security, and compliance across all data infrastructure.
• Performance Optimization: Continuously optimize data storage, retrieval, and processing performance to support business-critical operations.
• Innovation & Improvement: Stay updated with emerging data technologies and industry trends to enhance our data capabilities.

Technical Excellence & Operations

• System Reliability: Oversee the deployment, monitoring, and maintenance of data systems to ensure high availability and performance.
• DevOps & CI/CD: Implement agile methodologies, automated deployment pipelines, and DevOps best practices to streamline data operations.
• Problem Resolution: Lead efforts to troubleshoot and resolve complex data infrastructure challenges, collaborating with cross-functional teams when necessary.

Qualifications

• Experience: 7+ years in data engineering roles with significant experience in designing and managing data warehouses.
• Technical Expertise: Proven track record in building data warehouses on AWS using Snowflake, along with strong proficiency in ETL/ELT processes and modern data stack technologies (SQL, Python, etc.).
• Leadership: Demonstrated success in managing and scaling high-performing data engineering teams.
• Cloud & Architecture: Hands-on experience with cloud-based data solutions, microservices, and API-driven architectures.
• Methodologies: Solid understanding of agile development, DevOps practices, and CI/CD pipelines.
• Communication: Excellent analytical, problem-solving, and stakeholder management skills.

FAQ

1. What are the primary responsibilities of a Data Engineering Manager?
This role leads the design, development, and maintenance of data pipelines and data infrastructure. It involves ensuring reliable data ingestion, transformation, and availability for analytics and business use. The manager also drives best practices for scalability, performance, and data governance.

2. How much hands-on technical involvement is expected in this role?
While the role is primarily leadership-focused, strong technical oversight is required. You may review architecture, guide implementation decisions, and step in during complex technical challenges. Staying close to the technology ensures high-quality delivery.

3. What tools and technologies are commonly used in this position?
Common technologies include SQL, Python, Spark, and ETL/ELT tools such as Airflow or dbt. Cloud platforms like AWS, Azure, or Google Cloud are frequently used for data storage and processing. Data warehouses such as Snowflake, BigQuery, or Redshift are also key components.

4. How does this role collaborate with data scientists and analysts?
The Data Engineering Manager works closely with data scientists and analysts to ensure data is accessible, clean, and reliable. Collaboration helps align data pipelines with analytical and modeling needs. The role ensures that data infrastructure supports business use cases effectively.

5. What are the key responsibilities related to data architecture?
The role involves designing scalable data architectures, including data lakes, warehouses, and streaming systems. It ensures efficient data modeling and integration across systems. Maintaining performance and reliability is a critical part of this responsibility.

6. How is data quality managed in this role?
Data quality is maintained through validation checks, monitoring, and collaboration with upstream data sources. The manager ensures processes are in place to detect and resolve data issues जल्दी. Governance practices help maintain consistency and trust in data.