As a Data Engineer III, you will be part of an agile data services team responsible for developing and maintaining our industry-leading cloud-based big data and data analytics infrastructure serving major global fortune 500 companies. The candidate will help with design, development, unit testing, performance testing, deployment, troubleshooting, defect resolution, and support of our
Job Responsibilities
Utilize architectural and design skills for a variety of data-oriented tasks.
Establish and maintain strong relationships with business stakeholders.
Build highly reliable & scalable data systems with good data governance that provide complete, secure, accurate and timely data for analytics.
Expertise in data & dimensional modeling with good business acumen in supporting new product features and services - including financial reporting.
Assist development and analytics organization to meet its delivery goals with regards to data design, flow and infrastructure.
Perform Data Modeling, Big data architecture design, support and implementation of OLTP, real time OLAP/data warehouse systems and ETL pipelines.
Track/Build & monitor data/system KPI’s like storage, performance & usage
Hands on administration of big data databases/compute engines (Redshift, EMR, Spark or similar) and optimization/tuning techniques
Prepare required documentation as outlined by departmental standards.
Identify, analyze and back track data problems as needed.
Provide recommendations for application and system improvements.
Mentor other team members
Work with business users to understand business requirements, issues and business and/or client processes.
Develop, test, and maintain high-performance of our data systems to meet the requirements of the business and/clients while adhering to departmental standards.
Perform quality assurance testing for all work performed.
Meet with agile teams as required to define and document application requirements.
Follow project development, deployment process & maintain industry standards and best coding practices.
Maintain security and organization of the company’s data.
Provide off-hour support as assigned.
Gather business requirements/features, estimate the tasks, design & develop scalable solutions.
Work with manager to ascertain the company’s data requirements.
Plan work to meet project deadlines, accommodate demands by development teams, set priorities and escalate issues appropriately.
Provide recommendations to development teams for data restructuring and performing complex maintenance.
Deploy and manage changes in development, staging and production.
- Assist development and dev ops teams on SQL queries and tuning.
- Required Skills & Experience:
- At least 7 years’ experience in data engineering, Data modeling, SQL, ETL, Big database, Data warehousing & basic-level administration of data infrastructure
At least 7 years coding experience in Java, Python, R or other equivalent programming language
At least 4 years big data experience
At least 2 years’ experience as technical lead architecting data lake/data warehouse/lake house systems & mentoring team of Data Engineers / DBAs
Proficient in a variety of big-data tools and technologies, including Hadoop, Spark, etc., handling different storage formats like Parquet, Delta/Apache Hudi/Apache Iceberg, & managing/working with different OLTP systems & data ingestion mechanisms.
Experience building large scale data lakes & warehouse systems leveraging cloud environments including AWS technologies –S3, Redshift, Aurora/RDS, DMS, EC2, Glue spark, Cloud watch, & EMR
Experience with data orchestration tools like Airflow
Experience in building pipelines, ingesting data from heterogeneous systems onto data lake environments in real time
Proficient in Python, SQL and PL-SQL, query tuning, optimization, ETL, ELT, and Data Warehousing
Proficient in Business Intelligence (BI), analytic database and products, able to take business requirements and translate into database tables, pipeline design and tasks.
General data-specific concepts, frameworks and processes
Agile development practices
Working within an SDLC
Designing data warehouses including definition, structure, documentation, maintenance, long-range requirements, operational guidelines, and protection
- Data governance, meta data management, data security measures, retention & archival across data lake layers
- Other Skills and Experience
- Cloud experience, such as AWS, GCP or Azure
Building/Managing data lake & lake house architectures.
Experience working on different data lake storage formats.
Excellent oral and written communication
Multi-tasking and managing multiple priorities.
Working well in a team-oriented, collaborative environment with people from different disciplines and varying degrees of technical experience
- Working in an Agile team environment.
- Behavioral Competencies:
- Cultivates Innovation
Decision Quality
Manages Complexity
Drives Results
Business Insight
