Data Operations Lead - Charlotte, United States - Popular

Description

At Popular, we offer a wide variety of services and financial solutions to serve our communities in Puerto Rico, United States & Virgin Islands. As employees, we are dedicated to making our customers dreams come true by offering financial solutions in each stage of their life. Our extensive trajectory demonstrates the resiliency and determination of our employees to innovate, reach for the right solutions and strongly support the communities we serve; this is why we value their diverse skills, experiences and backgrounds.

Are you ready for a rewarding career?

Over 8,000 people in Puerto Rico, United States and Virgin Islands work at Popular.

Come and join our community

The Opportunity

As a Data Engineering & Data OPS Lead at our organization, you will play a pivotal role in propelling our Analytical Engineering, AI and Machine Learning initiatives to new heights. Your expertise will be crucial in ensuring the seamless operation and continual enhancement of Analytical Engineering data pipelines, AI/ML models and systems that are central to our data-driven decision-making and innovation. Your technical acumen will not only contribute to solving complex operational challenges but also foster a culture of excellence and continuous improvement within the team. This is a chance to strengthen the AI/ML operations, setting a high standard of operational excellence that aligns with our organizational goals and contributes to our position as a leader in the industry.

Your key responsibilities:

You will collaborate with multifaceted teams of specialists spread across various locations to offer a broad spectrum of data and analytics solutions. You will address complicated challenges and propel advancement within the Enterprise Data & Analytics function.

Specifically:

• Develop and execute the strategy for automating deployment process for Data Pipelines (DataOps) and Machine Learning Pipelines. The responsibility includes identification, evaluation, and adoption of emerging technologies and methodologies in DataOps / ModelOPs space.
• Lead a team of Analytical/ML Operations managers, specialists, providing mentorship, technical guidance, and career development opportunities.
• Lead the deployment, monitoring, maintenance, and continuous improvement of Analytical Engineering pipelines, AI/ML models and systems to ensure they operate reliably and deliver intended business value.
• Coordinate with corporate DevOps team in designing and building DataOps and ModelOps pipelines to automate and streamline the data pipelines and machine learning model development, Inventory management and deployment processes.
• Architect, design, implement, and optimize AI/ML pipelines for efficient data processing, model training, evaluation, and deployment.
• Partner with business continuity team and establish the strategy for Disaster Recovery management, defining RTO & RPO requirements, participating in regular DR drills, and multi-region deployments strategies.
• Collaborate closely with data scientists, machine learning engineers, and other stakeholders to understand AI/ML models, ensuring smooth transition from development to production.
• Manage relationships with external vendors and partners, ensuring alignment with organizational objectives and compliance with contractual obligations.
• Foster an environment that encourages innovation, knowledge sharing, and adoption of best practices in analytics and machine learning.
• Develop and implement robust monitoring systems to track the performance, accuracy, and reliability of Data pipelines, AI/ML models in production, providing insights and recommendations for improvement.
• Stay ahead of the curve with the latest advancements in AI/ML operations, tools, and best practices, advocating for the adoption of relevant technologies to enhance operational efficiency.
• Provide technical leadership in troubleshooting and addressing operational challenges, ensuring the stability and reliability of AI/ML systems.
• Document and standardize operational procedures, system configurations, and best practices for AI/ML operations, contributing to knowledge sharing and team training.
• Engage with external vendors, partners, and the broader AI/ML community to stay abreast of industry trends and innovations.
• Contribute to the development and enforcement of policies and procedures to ensure compliance with legal, privacy, and ethical considerations in AI/ML operations.

To qualify for the role, you must have:

• Bachelor's in computer science, Engineering, Statistics, Mathematics, or related fields. A master's degree in a related field is a plus.
• Minimum 20 years of experience in implementing large scale Data & Analytics platform in AWS, Azure, or Google Cloud, on-prem and Hybrid environment.
• Minimum 10 years of experience in leading and managing various functional team within ED&A such as data integration, data engineering, analytical engineering, BI / data visualization, Data Operations, or a similar role.
• Minimum 3 years of experience in AI/ML operations, DataOps, DevOps, or a related field.
• Managing team of 20 or more members with combination of full-time employees, contractors, and vendor resources across various locations in onshore, nearshore, and offshore and from multiple vendors.
• Experience in implementing pipelines and products in DevSecOps, DataOps, MLOps, and ModelOps such as ModelOp Center, Domino Data Lab, and Corridor Platform.
• Strong understanding of data pipelines, data architecture, and machine learning lifecycle management.
• Experience with DevOps and DataOps products such as Jenkins, Git, GITLab, Maven, Bitbucket, and Jira.
• Experience with log integration and observability products such as Splunk, Datadog, Grafana, AppDynamics, and CloudWatch.
• Experience with cloud platform design, sizing, optimization, platform upgrades, system patching, monitoring, and scripting.
• Hands-on experience with On-prem & cloud data platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, Databricks, AWS Aurora, Oracle Exadata, SQL server, Hadoop, Spark, SAS and R.
• Experience in implementing and managing scheduling & Orchestration tools such as AutoSys, ControlM, Apachi airflow, Step Functions and Tidal.
• Experience with AWS data & analytics services such as Batch, Glue, Athena, Data Marketplace, AppFlow, Step functions, Kinesis, Kafka, Airflow, DMS, Snow family, and open search.
• Deploy, configure and managing workbenches such as RStudio, Jupyter notebook, CDSW, and Visual Studio.
• Excellent problem-solving skills, with the ability to troubleshoot and address operational challenges in AI/ML systems.
• Proficiency in relevant programming languages such as Python, R, or Java, and experience with AI/ML frameworks like TensorFlow, PyTorch, or similar.
• Strong communication skills, with the ability to collaborate effectively with cross-functional teams and explain complex technical issues to non-technical stakeholders.
• Continuous learner, staying updated with the latest advancements in DataOps, AI/ML operations, tools, and technologies.
• Proficiency in data integration tools and frameworks such as Informatica PC & IICS, IBM DataStage, DBT, Matillion, Microsoft SSIS, Glue, Batch, Azure data factory, data pipeline, Qlik replicate, Oracle GoldenGate, Shareplex, Apache NiFi and Python based frameworks.
• Excellent data analysis, profiling and statistics skills coupled with proficiency in SQL tools and technologies such as Oracle, SQL Server, MySQL, Pandas, NumPy, Ggplot, Shiny, SciPy, Sci-Kit Learn, and Matplotlib.
• Strong proficiency in Hive, SQL, Spark, Python, R, SAS or other data manipulation and transformation languages.
• Experience in handling data streams, APIs, events, container orchestration products such OpenShift, EKS, ECS.
• Experience in Cloud transformation and implemented various strategies such as Rehost, Re-platform, Repurchase, Refactor / Re-architect, Retire, and Retain.
• Hands-on experience in designing and building data pipelines by leveraging AWS services such as S3, S3 Glacier, EC2, ECS, EMR, Sagemaker, IAM, RDS, DynamoDB, Hive, GraphDB, and DocumentDB.
• Experience with monitoring tools and platforms, and a strong understanding of metrics, logging, and alerting in AI/ML operations.
• Familiarity with ethical, legal, and privacy considerations in AI/ML operations is a plus.
• Implementation experience of one or more AI/ML platforms in cloud such as Sagemaker, Dataiku, DataRobot, , Snowpark, ModelOp Center, and Domino Data Lab.

What we look for:

We are seeking enthusiastic and proactive leaders who have a clear vision and an unwavering commitment to remain at the forefront of data technology and science. Our ideal candidates are those who aim to foster a team spirit and collaboration and have a knack for adept management. It is essential that you display comprehensive technical proficiency and possess a rich understanding of the financial services industry.

If you have a genuine drive for helping consumers achieve the full potential of their data while working towards your own development, this role is for you.

Important: The candidate must provide evidence of academic preparation or courses related to the job posting, if necessary.

If you have a disability and need assistance with the application process, please contact us . This email inbox is monitored for such types of requests only. All information you provide will be kept confidential and will be used only to the extent required to provide reasonable accommodations. Any other correspondence will not receive a response.

As a leading financial institution in the communities we serve, we reaffirm our commitment to always offer essential financial services and solutions for our customers, including during emergency situations and/or natural disasters. Popular's employees are considered essential workers, whose role is critical in the continuity of these important services even under such circumstances. By applying to this position, you acknowledge that Popular may require your services during and immediately after any such events.

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Popular is an Equal Opportunity Employer

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