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Job Description for Senior Data Engineer

Objectives and Purpose

The Senior Data Engineer ingests, builds, and supports large-scale data architectures that serve multiple downstream systems and business users. This individual supports the Data Engineer Leads and partners with Visualization on data quality and troubleshooting needs.
 

The Senior Data Engineer will:

• Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
• Support development testing and maintenance of data pipelines and platforms, to enable data quality to be utilized within business dashboards and tools.
• Create, maintain, and support the data platform and infrastructure that enables the analytics front-end; this includes the testing, maintenance, construction, and development of architectures such as high-volume, large-scale data processing and databases with proper verification and validation processes.

Your key responsibilities

Data Engineering

• Design, develop, optimize, and maintain data architecture and pipelines that adheres to ETL principles and business goals.
• Develop and maintain scalable data pipelines, build out new integrations using AWS native technologies to support continuing increases in data source, volume, and complexity.
• Define data requirements, gather and mine large scale of structured and unstructured data, and validate data by running various data tools in the Big Data Environment.
• Support standardization, customization and ad hoc data analysis and develop the mechanisms to ingest, analyze, validate, normalize, and clean data.
• Write unit/integration/performance test scripts and perform data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
• Implement processes and systems to drive data reconciliation and monitor data quality, ensuring production data is always accurate and available for key stakeholders, downstream systems, and business processes.
• Lead the evaluation, implementation and deployment of emerging tools and processes for analytic data engineering to improve productivity.
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
• Learn about machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics.
• Solve complex data problems to deliver insights that help achieve business objectives.
• Implement statistical data quality procedures on new data sources by applying rigorous iterative data analytics.

Relationship Building and Collaboration

• Partner with Business Analytics and Solution Architects to develop technical architectures for strategic enterprise projects and initiatives.
• Coordinate with Data Scientists to understand data requirements, and design solutions that enable advanced analytics, machine learning, and predictive modelling.
• Support Data Scientists in data sourcing and preparation to visualize data and synthesize insights of commercial value.
• Collaborate with AI/ML engineers to create data products for analytics and data scientist team members to improve productivity.
• Advise, consult, mentor and coach other data and analytic professionals on data standards and practices, promoting the values of learning and growth.
• Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.

Skills and attributes for success

Technical/Functional Expertise

• Advanced experience and understanding of data/Big Data, data integration, data modelling, AWS, and cloud technologies.
• Strong business acumen with knowledge of the Pharmaceutical, Healthcare, or Life Sciences sector is preferred, but not required.
• Ability to build processes that support data transformation, workload management, data structures, dependency, and metadata.
• Ability to build and optimize queries (SQL), data sets, 'Big Data' pipelines, and architectures for structured and unstructured data.
• Experience with or knowledge of Agile Software Development methodologies.

Leadership

• Strategic mindset of thinking above the minor, tactical details and focusing on the long-term, strategic goals of the organization.
• Advocate of a culture of collaboration and psychological safety.

Decision-making and Autonomy

• Shift from manual decision-making to data-driven, strategic decision-making.
• Proven track record of applying critical thinking to resolve issues and overcome obstacles.

Interaction

• Proven track record of collaboration and developing strong working relationships with key stakeholders by building trust and being a true business partner.
• Demonstrated success in collaborating with different IT functions, contractors, and constituents to deliver data solutions that meet standards and security measures.

Innovation

• Passion for re-imagining new solutions, processes, and end-user experience by leveraging digital and disruptive technologies and developing advanced data and analytics solutions.
• Advocate of a culture of growth mindset, agility, and continuous improvement.

Complexity

• Demonstrates high multicultural sensitivity to lead teams effectively.
• Ability to coordinate and problem-solve amongst larger teams.

To qualify for the role, you must have the following:

Essential skillsets

• Bachelor’s degree in Engineering, Computer Science, Data Science, or related field
• 5+ years of experience in software development, data science, data engineering, ETL, and analytics reporting development
• Experience designing, building, implementing, and maintaining data and system integrations using dimensional data modelling and development and optimization of ETL pipelines
• Proven track record of designing and implementing complex data solutions
• Demonstrated understanding and experience using:
  • Data Engineering Programming Languages (i.e., Python)
  • Distributed Data Technologies (e.g., Pyspark)
  • Cloud platform deployment and tools (e.g., Kubernetes)
  • Relational SQL databases
  • DevOps and continuous integration
  • AWS cloud services and technologies (i.e., Lambda, S3, DMS, Step Functions, Event Bridge, Cloud Watch, RDS)
  • Databricks/ETL
  • IICS/DMS
  • GitHub
  • Event Bridge, Tidal
• Strong organizational skills with the ability to manage multiple projects simultaneously and operate as a leading member across globally distributed teams to deliver high-quality services and solutions
• Understanding of database architecture and administration
• Processes high proficiency in code programming languages (e.g., SQL, Python, Pyspark, AWS services) to design, maintain, and optimize data architecture/pipelines that fit business goals
• Extracts, transforms, and loads data from multiple external/internal sources using Databricks Lakehouse/Data Lake concepts into a single, consistent source to serve business users and data visualization needs
• Utilizes the principles of continuous integration and delivery to automate the deployment of code changes to elevate environments, fostering enhanced code quality, test coverage, and automation of resilient test cases
• Excellent written and verbal communication skills, including storytelling and interacting effectively with multifunctional teams and other strategic partners
• Strong problem solving and troubleshooting skills
• Ability to work in a fast-paced environment and adapt to changing business priorities

Desired skillsets

• Degree in Engineering, Computer Science, Data Science, or related field
• Experience in a global working environment

Travel requirements

• Access to transportation to attend meetings
• Ability to fly to meetings regionally and globally

EY | Building a better working world 

 
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.  

 
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