Key Areas of Responsibility
• Responsible for building and managing end-to-end data pipelines and operations from ingestion and integration through delivery for the data products.
• Build cross-functional relationships with Business Stakeholders, Architects, Data Scientists, Product Managers and IT to understand data needs and deliver on those needs.
• Drive the design, building and launching of new data models and data pipelines in production.
• Manage the development of data resources and support new product launches.
• Lead discussion of product-oriented analysis in meetings with clients and partners; comfortable speaking to executives.
• Primary data liaison for stakeholders to drive transformation and to democratize use of data.
• Sunset multiple redundant warehouses and marts with significant cost savings and support new integration and modernization.
• Consolidate the fragmented data across the company and provide simplified access to data for the stakeholders, internal users as well as external partners.
• Support compliance and auditing through a single gateway for data exchange.
• Stay abreast of technology development in retail and other industries.
• Act as a sounding board on testing, experimentation, target audience profiling and consumer insights that analyze the relationship between customers, products, partners, conversions, engagement and revenue and drivers.
• Work with multiple complex and disparate datasets to enable data delivery through various means and APIs to evaluate performance and amalgamate information to derive strategic insights and recommendations.
• Contribute and support the development of the overall data science and machine learning strategy and roadmap.
• Establish the core data foundation and common data lake to enable data driven decisions.
• Support delivery of scalable data products.
• Actively participate in the industry externally through internet research, white papers, or conferences.
Education and/or Experience Qualifications
• Bachelor’s degree in computer science, Information Systems or equivalent IT knowledge/experience.
• 5+ years of relevant work experience as a Data Engineer.
Other Required Qualifications
• Experience working in Data engineering and ETL teams and managing implementation projects that utilize big data, advanced analytics and machine learning technologies.
• Experience with agile software development methodologies.
• Ability to manage onshore and offshore resources.
• Distributed architecture and SaaS experience.
• Hands-on experience in building pipelines from variety of sources such as data warehouses and in-memory OLAP models, as well as experience in
• Strong understanding of data and information architecture, including experience with Big Data, Relational databases, streaming and batch data processing.
• Strong experience building end-to-end data view with focus on integration.
• Ability to effectively present information, interact with, and respond to questions from managers, employees, customers, and vendors.
• Demonstrated experience in teaching and/or mentoring professionals.
• Passion to evangelize data science and engineering, teach others and learn new techniques.
Data Science and Advanced Analytics Required Qualifications
• Expert Level -Data Exploration and ETL: Alteryx, TalenD, H2O, Informatica, Data Stage etc.
• Expert Level - Experience with programming languages use (Spark, Python, R, Jupyter Notebooks, Java, Scala).
• Expert Level -Data Warehouse Solutions: Redshift, Snowflake, Postgres
• Expert Level - Big Data technologies, Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB).
• Expert Level -Workflow management: Airflow, Oozie, Azkaban
• Advanced Level -Cloud storage: S3, GCS
• Advanced Level – Github, Maven etc. – Modern code organizer and build process for about half of our applications
• Advanced Level – Expert at Jenkins – Modern build executor
• Advanced Level – Containers – Modern build with microservices
• Advanced Level – Swagger – Experience with modern features for the API including an automatically generated user interface
• Beginner Level -Data Visualization Solutions: Looker, Tableau etc.
• Beginner Level -Distributed logging systems: Pulsar, Kinesis etc.
• Beginner Level -Data Science Workbenches: Cloudera, SAS etc.
• Experience working for consumer or business-facing digital brands.
• Bachelor’s degree in Business, Math, Engineering, Statistic, Economics, Operation Research, Data Science, Computer Science or related quantitate field