ADP is hiring a Sr. Lead Analytical Data Engineer for our newly formed HR Outsourcing (HRO) Data and Analytics organization. The Sr. Lead Analytical Data Engineer will play a key role in growing our newest chapter of analytics engineering professionals while interacting with cross-functional teams to address complex business requirements. We’re seeking a value seeking, self-motivated, and analytical profession who act as a player and coach to multiple workstreams at the same time. The role demands the individual to possess technical skills required to perform the job in an effective manner. The right candidate will be a technical expert, should have the passion for data & analytics and works along with the team they manage.

PREFERRED QUALIFICATIONS

• Proficiency in DataBricks: Hands-on experience and strong technical knowledge with DataBricks, particularly focusing on Apache Spark fundamentals, including Spark Architecture, its API’s, and how to leverage it for data processing and analysis. Proficiency in Pyspark/Python/SQL is essential for data manipulation, analysis, and transformation.
• AWS Certification: AWS certification is highly desirable, reflecting your expertise in cloud technologies and services.
• Framework Development: Experience in developing frameworks and utility services, including logging/monitoring is an asset, demonstrating your ability to create efficient and scalable solutions.
• Programming Skills: Proficiency in UNIX and the Python programming language is essential, as it is fundamental for data engineering tasks.
• Software Development Best Practices: Proven experience in delivering high-quality software following continuous delivery practices and using code quality tools such as JIRA, GitHub, and Jenkins is a strong advantage.
• Data Storage Solutions: Comfort with a variety of data storage solutions, including RDBMS (e.g., Oracle), Hive, HBase, Impala, and other options, showcases your versatility in handling different data storage needs.
• NoSQL Databases: Knowledge of NoSQL databases like MongoDB, HBase, Cassandra, etc., is a plus, highlighting your familiarity with a range of data storage technologies.
• Project Management Tools: Familiarity with Jira and Confluence is preferred, as it facilitates effective project management and documentation.
• Data Solutions Architecture: While not mandatory, experience as a data solutions architect is considered a significant advantage, demonstrating your ability to design comprehensive data solutions.
• Cloud Database Technologies: Experience with cloud database technologies and the ability to develop solutions on cloud computing services and infrastructure in the data and analytics space is a valuable skill set.
• PySpark Expertise: Comfort with using PySpark APIs to perform advanced data transformations is a key technical requirement.

RESPONSIBILITIES:

• Strategic Data Solution Development: Support the development of foundational data with a strategic outlook, focusing on the broader scope of building and enhancing domains within the semantic layer of HRO.
• Documentation and Best Practices: Catalog existing analytics and automation processes and provide recommendations for best practice methods to optimize and improve these processes.
• Collaborative Stakeholder Engagement: Collaborate closely with the Business Transformation & Data Analytics Leadership to execute project plans within a fast-paced agile development environment.
• Data Engineering Expertise: Create foundational data by skillfully engineering critical elements from multiple data sources. Ensure the ingestion of HRO-specific data is well-structured, compliant with data quality standards, and traceable from the consumption layer back to the raw data layer.
• Strategic Integration and Security: Partner with the Data Operations, Data Governance and Business Intelligence and Data Science teams to streamline data integration, maintain data security, and access best practices.
• Technological Advancement: Conduct thorough research to identify and recommend cutting-edge technologies and processes that support rapid scaling and future growth initiatives.
• Prioritization and Delivery: Assist the team in prioritizing business needs, leadership questions, and ad hoc requests to ensure on-time delivery and alignment with organizational goals.
• Quality Assurance: Take the lead in driving quality assurance and data quality efforts to enhance development timelines, reduce bugs, and maintain the reliability of our data products.
• Iterative Development: Bring your experience in developing v0.5 solutions, incorporating real-world feedback, and iterating to v1.0+ with a continuous improvement mindset.
• Exceptional Delivery: Leverage your successful track record of superior delivery and change management within an enterprise organization to drive positive change and growth.

TO SUCCEED IN THIS ROLE:

• Educational Background: Possess a bachelor’s degree in computer science, engineering, business, statistics, or related fields; advanced degrees are preferred but not mandatory.
• Experience: Demonstrate a strong foundation in data analytics, engineering, and project management with a minimum of 10 years of hands-on experience in the implementation, development, improvement, and support of data-related projects.
• Matrix Organization Experience: 3+ years of experience working in a matrix organization, where you have a product/pod leadership directing the team to achieve use case goals.
• Data Expertise: Exhibit deep experience with ETL (Extract, Transform, Load), Data Modeling, and Data Architecture, highlighting your ability to design and maintain data solutions.
• Problem-Solving Aptitude: Prove your ability to lead a team in leveraging data, analytics, and business acumen to address intricate business challenges.
• Data Platform Experience: Possess hands-on experience with DataBricks, and technologies such as Hadoop, Spark. Demonstrate expertise in managing data housed in various relational databases.
• Data Pipeline Proficiency: Showcase your competence in building data pipelines and deploying/maintaining them using tools like Git and Jenkins.
• Data Analytics Skills: Demonstrate experience and expertise in data mining methods, data modeling, and working with data warehouses, showcasing your ability to extract valuable insights.
• Technical Skills: Exhibit mastery in Pyspark and SQL, as well as an understanding of agile methodologies and the key factors that contribute to a team’s success.