Senior Manager - Data Engineering
Location: Bangalore (assumed)
About LeadSquared
LeadSquared is a leading Sales Execution and Marketing Automation platform empowering organizations with automation and AI/ML-driven data solutions.
Responsibilities:
- Strategic Leadership: Develop and champion data engineering strategy aligned with company's data lake vision, AI/ML objectives, and business goals.
- Team Building and Management: Recruit, mentor, and lead multiple data engineering teams, fostering technical excellence, collaboration, and continuous improvement.
- Organizational Scaling: Design and implement structure and processes to scale data engineering for growing AI/ML demands.
- AI/ML Data Exposure: Understand data requirements for AI/ML techniques (supervised, unsupervised learning, deep learning, NLP) and design pipelines accordingly.
- Technology Selection: Evaluate and recommend data technologies and tools (cloud platforms, data warehouses, lakes, ETL/ELT tools, streaming platforms, feature stores).
- Pipeline Development: Oversee development and implementation of scalable, reliable data pipelines using Spark, Flink, Kafka, Airflow, or similar.
- Data Quality and Governance: Establish data quality standards, enforce governance policies, ensure data security and compliance.
- Performance Optimization: Identify and resolve pipeline and infrastructure bottlenecks for efficient data processing.
- Cloud Expertise: Leverage AWS (or other cloud) data services to build scalable, cost-effective solutions.
- Documentation: Maintain comprehensive documentation for pipelines, models, and infrastructure.
- Innovation: Stay current on trends and technologies in data engineering and AI/ML, propose innovative solutions.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- 10+ years experience in data engineering with a focus on building data solutions.
- Proven leadership and mentoring experience for data engineering teams.
- Deep knowledge of data warehousing, data modeling, SQL and NoSQL databases.
- Proficient in Java, Go, Python, and SQL programming languages.
- Hands-on with big data frameworks such as Spark or Flink.
- Experience with ETL/ELT tools and data integration technologies.
- Experience with streaming platforms like Kafka or Kinesis.
- Solid understanding of cloud platforms (AWS, Azure, GCP) and data services.
- Experience with feature stores and feature engineering pipelines preferred.
- Strong understanding of data governance and quality management.
- Excellent problem-solving, analytical, and communication skills.
- Ability to work independently and collaboratively in a fast-paced environment.
