Staff Data Engineer SAAS

Job Summary:

The Staff Data Engineer works on different projects of data engineering to support the use cases, data ingestion pipeline, data warehouse, data lake and enterprise big data platform with multi-datacenter contexts and identify potential process or data warehousing issues. For the SaaS Administrator role, the resource will serve as the primary administrator for enterprise SaaS platforms supporting data governance and privacy compliance, managing user roles, permissions, and access controls in accordance with security and compliance policies and monitoring system performance, conducting regular audits, and implementing updates and patches.

From a data governance & MDM perspective they will design and maintain data models, business rules, workflows, and data validation logic. Oversee data ingestion processes and integrations with internal and external systems and support data profiling, cleansing, and enrichment initiatives.

Minimum Qualifications:

• Bachelor's Degree in Computer Science or Information Systems or equivalent, relevant work experience

• 8+ years or more of work experience required in IT, SQL, Hadoop Ecosystems, Data warehouse, Data Modelling

• Experience on project management framework Agile (Jira toolset). Knowledge and experience in developing software using agile methodologies.

• Expert in ER studio, Business process management tool. Expert knowledge on metadata management and related tools

Preferred Qualifications:

• Experience with cloud platforms (AWS & Azure)

• Strong understanding of data governance, data quality, and privacy regulations

• Integration experience with Kafka, REST APIs and JSON

• Excellent problem-solving and communication skills

• Reltio certifications

• Familiarity with Agile methodologies and DevOps practices

Roles and Responsibilities

Essential Job Functions:

• Demonstrate foundational knowledge of credit card operations, banking, financial, loyalty rewards, retail, and credit card regulations while working with the business. Understand and employ common procedures and tools used in the Data Operations lifecycle (Agile Project Management, CI/CD, JIRA, etc.). Document important milestones and data lineage definitions. - (10%)

• Provides guidance on building data pipeline for various data sources, particularly from many external data sources. Works with other project team members to understand the use cases, familiar with data sources, provide recommendation on data ingestion processes, complete data ingestion, monitor and identify potential process or data quality issues. - (15%)

• Collaborate with Data Architects/Engineers on data model management and version control. Capture Critical Data Elements (CDE) and translate them into quality checks with Business/Data Engineers. Collaborate with business users, analysts, and architects to convert requirements into analytical tools and dashboards, aligning with usability best practices and design trends. - (10%)

• Demonstrates analytical, interpersonal and professional communication skills. Learns quickly and works effectively individually and as part of a team. Accesses, extracts, and transforms Credit and Retail data from a variety of sources of all sizes (including client marketing databases, 2ndand 3rdparty data) using Hadoop, Spark, SQL, Big data technologies etc. - (10%)

• Provides automation help to analytical teams around data centric needs using orchestration tools, SQL and possibly other big data/cloud solutions for efficiency improvement. Supports Lead Data Engineer and Principal Data Engineer in new analytical proof of concepts and tool exploration projects. - (5%)

• Effectively manages time and computing resources in order to deliver on time/correctly on concurrent projects. Involved in creating POCs to ingest and process streaming data using Spark and HDFS. Answers and trouble shoots questions about data sets and analytical tools; Develops, maintains and enhances new and existing analytics tools/Frameworks to support internal customers/consumers. - (10%)

• Design, develop and implement data infrastructure and pipelines that collect, connect, centralize, and curate data from various internal and external data sources. Create automation systems and tools to configure, monitor, and orchestrate our data infrastructure and our data pipelines. - (10%)

• Ingests data from different sources, processes it according to the requirement document in order to store data to Hive or NoSQL database or different warehousing solutions. Involved in HDFS maintenance, and loading of structured and unstructured data. Evaluate new technologies for continuous improvements and collaborate closely with the product team to build out new data features. - (10%)

• Work with the data analysts and scientists to implement descriptive, forecasting, and predictive algorithms and models using the latest technologies. Make technology decisions for our data infrastructure. Applies knowledge in Agile Scrum methodology that leverages the Client BigData platform and used version control tool Git. - (10%)

• Perform as a Subject Matter Expert on various data domains and business processes. Understand and employ best practices in the field. Educate and mentor associates across the business in these best practices. Translate complex technical subjects for diverse audiences, ensuring alignment and understanding across technical and non-technical stakeholders. - (10%)