WHAT YOU'LL DO
- Design, document & implement the data pipelines to feed data models for subsequent consumption in Snowflake using dbt, airflow .
- Ensure correctness and completeness of the data being transformed via engineering pipelines for end consumption in Analytical Dashboards.
- Actively monitor and triage technical challenges in critical situations that require immediate resolution.
- Evaluate viable technical solutions and share MVPs or PoCs in support of the research
- Develop relationships with external stakeholders to maintain awareness of data and security issues and trends
- Review work from other tech team members and provide feedback for growth
- Implement Data Performance, Data security policies that align with governance objectives and regulatory requirements
YOU'RE GOOD AT
You have experience in data warehousing, data modeling, and the building of data engineering pipelines. You are well versed in data engineering methods, such as ETL and ELT techniques through scripting and / or tooling.
You are good in analyzing performance bottlenecks and providing enhancement recommendations; you have a passion for customer service and a desire to learn and grow as a professional and a technologist.
- Strong analytical skills related to working with unstructured datasets.
- Collaborating with product owners to identify requirements, define desired outcomes and deliver trusted results.
- Building processes supporting data transformation, data structures, metadata, dependency and workload management.
- In this role, SQL is heavily focused. An ideal candidate must have hands-on experience with SQL database design. Plus, Python .
- Demonstrably deep understanding of SQL (level : advanced) and analytical data warehouses ( Snowflake preferred).
- Demonstrated ability to write new code i.e., well-documented and stored in a version control system (we use GitHub & Bitbucket )
- Extremely talented in applying SCD, CDC and DQ / DV framework.
- Familiar with JIRA & Confluence .
- Must have exposure to technologies such as dbt, Apache airflow, and Snowflake .
- Desire to continually keep up with advancements in data engineering practices.
- Knowledge of AWS cloud, and Python is a plus.
YOU BRING (EXPERIENCE & QUALIFICATIONS)
Essential Education
- Bachelor's degree or equivalent combination of education and experience.
- Bachelor's degree in information science, data management, computer science or related field preferred.
Essential Experience & Job Requirements
- 5+ years of IT experience with major focus on data warehouse / database related projects
- Must have exposure to technologies such as dbt, Apache Airflow, Snowflake.
- Experience in data platforms : Snowflake, Oracle, SQL Server, MDM etc
- Expertise in writing SQL and database objects - Stored procedures, functions, and views. Hands-on experience in ETL / ELT and data security, SQL performance optimization and job orchestration tools and technologies e.
g., dbt, APIs, Apache Airflow, etc.
- Experience in data modeling and relational database design
- Well-versed in applying SCD, CDC, and DQ / DV framework.
- Demonstrate ability to write new code i.e., well-documented and stored in a version control system (we use GitHub & Bitbucket)
- Good to have experience with Cloud Platforms such as AWS, Azure, GCP and Snowflake
- Good to have strong programming / scripting skills (Python, PowerShell, etc.)
- Experience around responsibility for analyzing and interpreting worker, candidate, or HR, Workday data, to provide valuable insights and support strategic decision-making.
- Experience working with agile methodologies (Scrum, Kanban) and Meta Scrum with cross-functional teams (Product Owners, Scrum Master, Architects, and data SMEs)
- Excellent written, and oral communication and presentation skills to present architecture, features, and solution recommendations
YOU'LL WORK WITH
- Global functional product portfolio technical leaders (Finance, HR, Marketing, Legal, Risk, IT), product owners, functional area teams across levels
- Global Data Product Portfolio Management & teams (Enterprise Data Model, Data Catalog, Master Data Management)
- Consulting and internal Data Product Portfolio teams across BCG
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