Data Engineering Manager

Role Overview

This role will be part of the team responsible for developing and deploying Data Engineering Platform and Integration Solutions. The Data Engineering Manager will work closely with the Data Engineering team, Data Architects, and AI Team to implement data solutions for the organization using Python, PySpark, and other big data solutions.

What You Will Do

Design and implement data structures, workflows, and integrations between enterprise platforms to ensure the accurate and timely execution of business processes. Maintain scalable data pipelines to support continuing increases in data volume and complexity.

Why It Might Be a Fit

The ideal candidate will have experience with advanced analytics tools, database development, and data governance. They will be able to work with business users, technology teams, and executives to understand their data needs and create innovative solutions to fulfill them.

Requirements

• Minimum Graduation Degree and 12+ years’ related experience
• Experience with schema designing, data modeling, designing, building, and maintaining data processing systems
• Experience with advanced analytics tools for Object-Oriented/object function scripting using languages such as Python and PySpark
• Database development experience using ETL Process, SQL, SPARK, or BigQuery and experience with Delta Lakes and Data Warehouse
• Experience in triaging data issues, analyzing end-to-end data pipelines and working with business users in resolving issues
• Experience in working with data governance/data quality and data security teams and specifically data stewards and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification
• Exposure to Big Data Development using various tools & techniques Databricks, Snowflakes, OCI, Hive, Impala, Spark, etc.
• Exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics
• Exposure to agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines
• Excellent communication skills (verbal, listening and writing)
• Ability to build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management
• Ability to work with both IT and business in integrating analytics and data science output into business processes and workflows
• An agile learner who brings strong problem-solving skills and enjoys working as part of a technical, cross functional team to solve complex data problems
• Able to prioritize and manage multiple projects and requests at any one time
• Strong attention to detail when identifying data relationships, trends, and anomalies
• Thinking through long-term impacts of key design decisions and handling failure scenarios
• Ability to effectively share technical information, communicate technical issues and solutions to all levels of business
• Ability to meet strict deadlines, work on multiple tasks and work well under pressure

Benefits

• Medical, dental and vision coverage with low deductible & copay
• Life insurance
• Short and long-term disability
• Paid Parental Leave
• 401(k) + match
• Employee Stock Purchase Plan
• Generous Paid Time Off – accrued based on years of service
• 10 paid company holidays
• Tuition reimbursement
• Flexible Spending Account
• Employee Assistance Program
• Sick time benefits – for eligible employees, one hour of sick time for every 30 hours worked, up to a maximum accrual of 40 hours per calendar year

Originally posted on Himalayas