Job Description

• Manage a team of data engineers, ETL developers, and vendor consulting resources to ensure successful execution of data engineering delivery projects, provide governance and subject matter expertise support for data engineering platform
• Responsible and accountable for data engineering team's deliverables.
• Identify, coach, and groom the second level leadership within the team.
• Identify process gaps and come up with creative solutions to fill the gaps in the delivery process.
• Should be able to solve delivery issues with tactical and strategic mind set.
• Should be good motivator with excellent people management skills.
• Should be able to help the team both technically and professionally in their career path.
• Enforce the established best practices, standards across the active projects with Quality and "Customer At First" mind set.
• Lead, design, develop, deploy, and maintain mission-critical data applications for Enterprise data platform
• Participate in all phases of the Enterprise Data platform development life cycle as appropriate; including, but not limited to gathering customer requirements, defining technical requirements, creating high-level architecture diagrams, data validation, and training sessions
• Engage in Data solutions and Business Intelligence Projects and drive them to closure
• Drive the team to lead different aspects of data quality, machine learning, data acquisition, and some design and analysis tasks

• Have strong experience in data engineering, data transformation, big data or business intelligence products
• Ability to work independently and as a member of a cross-functional team
• Lead and manage data engineers within the team
• Is passionate about applications, data analytics, end-user productivity
• Exceptional customer focus
• Desire to teach other team members about technology in the area of expertise
• Experience in project management framework Agile (Jira toolset)

• Master's degree in computer science engineering from an accredited university (desired)
• 4-year degree with a major in computer science engineering (or equivalent) from an accredited university (preferred) will substitute for a minimum of 8-10 years' professional IT experience.

• Experience in Big Data, Data lake, Oracle, SQL Server, or AWS Redshift type databases
• Experience in ETL/ELT(Data extraction, data transformation, and data load processes)
• Strong Experience in DevOps

• 5+ Years of Experience in Data Lake, Data Analytics & Business Intelligence Solutions
• 2-4 years of experience at Enterprise-level data engineering leveraging python, spark, databricks, Informatica Power Center 9.x, 10.x, and Informatica Cloud environment.
• 2+ years of working experience in a DevOps environment, data integration, and pipeline development.
• 2+ years of Experience with AWS Cloud on data integration with Apache Spark, EMR, Glue, Kafka, Kinesis, and Lambda in S3, Redshift, RDS, MongoDB/DynamoDB ecosystems
• Demonstrated skill and ability in the development of data warehouse projects/applications (Oracle & SQL Server)
• Strong real-life experience in python development especially in pySpark in AWS Cloud environment.
• Strong analytical experience with the database in writing complex queries, query optimization, debugging, user-defined functions, views, indexes, etc.
• Experience with source control systems such as Git, and Jenkins build and continuous integration tools.
• Knowledge of extract development against ERPs - SAP, SFDC, JDE, preferred
• Exposure to data visualization tools like Power BI, Tableau, etc

• Highly self-driven, execution-focused, with a willingness to do "what it takes" to deliver results as you will be expected to rapidly cover a considerable amount of demands on data integration
• Strategic Thinking - Think big picture. Set priorities aligned with major goals. Encourage innovation by backing good people who take smart risks.
• Critical Thinking - Question conventional wisdom by identifying and challenging assumptions made that cause actions or inaction. Strive to inject independent thinking, checking biases, promotes action and decision-making.
• Communication - communicate effectively in the way reach audiences with ease, clarity, and transparency: one-to-one, small group, full staff, email, social media, and of course, listening.
• Provide organizational support for relationship development to foster teamwork, build relationships, and promote collaboration to cultivate and strengthen a network for the exchange of ideas
• Problem-solving (analytical)