Sr. Manager - Data Engineering

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.