12 mar
Allkem
Buenos Aires
Data Engineering Lead - EY Global Delivery Services
EY is a global leader in assurance, tax, transaction, and advisory services. Technology is at the heart of what we do and deliver at EY. Technology solutions are integrated into the client services we deliver and are key to our innovation as an organization. Fueled by strategic investment in technology and innovation, Client Technology seeks to drive growth opportunities and solve complex business problems for our clients through building a robust platform for business and a powerful product engine that are vital to innovation at scale.
As part of Client Technology, you’ll work with technologists and business experts,
blending EY’s deep industry knowledge and innovative ideas with our platforms, capabilities, and technical expertise. As a catalyst for change and growth, you’ll be at the forefront of integrating emerging technologies from AI to Data Analytics into every corner of what we do at EY.
Your key responsibilities include:
1. Leading the design, development, and implementation of processes to extract, transform, and load data from disparate sources into a form that is consumable by analytics processes, reviewing deliverables to ensure high quality.
2. Leading the translation of requirements, design, and solution architecture deliverables into detailed design specifications.
3. Evaluating and resolving issues regarding data quality reviews, cleansing, data integration, and migration, leveraging advanced technical knowledge and showing technical leadership in aspects of data engineering while driving continuous improvement efforts.
4. Providing a leadership role for the work group, ensuring the appropriate expectations, principles, structures, tools, and responsibilities are in place to deliver the project.
5.
Analyzing the latest industry trends such as cloud computing and distributed processing and inferring potential impact on businesses (short and long-term).
6. Providing advanced technical expertise to maximize efficiency, reliability, and value from current data engineering processes. Researching and monitoring existing client base and industry developments to identify potential new product opportunities from emerging technologies.
7. Developing strong working relationships with peers across engineering, collaborating to develop leading data engineering solutions.
8. Driving adherence to the relevant data engineering and data modeling processes, procedures, standards, and may input into the definition, maintenance, and implementation of technology standards.
Skills and attributes for success:
1. Batch Processing:
Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period.
2. Data Integration (Sourcing, Storage, and Migration): Capability to design and implement models, capabilities, and solutions to manage data within the enterprise (structured and unstructured, data archiving principles, data warehousing, data sourcing, etc.). This includes the data models, storage requirements, and migration of data from one system to another.
3. Data Quality, Profiling, and Cleansing: Capability to review (profile) a data set to establish its quality against a defined set of parameters and to highlight data where corrective action (cleansing) is required to remediate the data.
4. Stream Systems: Capability to discover, integrate, and ingest all available data from the machines that produce it, as fast as it’s produced,
in any format, and at any quality.
Required Technical Skills:
1. Experience designing and building Data Platforms integrating disparate data sources.
2. Knowledge of Core Java/Scala.
3. Expertise in Data Architecture, ETL, SQL.
4. Expertise in working with MPP designs to speed the performance of huge databases that deal with massive amounts of data.
5. Expertise in Azure, Azure Data Bricks, Azure SQL, Spark.
6. Expertise developing dataflows using NiFi/ADF, Databricks.
7. Advanced, hands-on design experience on implemented large analytic warehouse.
8. Advanced, hands-on experience in Spark architecture and implementation using several methods.
9. Experience in working with Distributed Message Systems like Kafka.
10. Hands-on experience in Python, Pyspark, or R.
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