As an Engineer at Invesco, you would align business outcomes to technology goals and objectives. You will be responsible for managing decision sciences and advanced analytics initiatives, data integration/engineering. Interacts with Data Scientists, Business Owners, Business Data Analysts, Data Modelers, Architects, and Application Developers, to design, build and handle large-scale batch and real-time data pipelines utilizing various data analytics processing frameworks in support of Data Science practice. The position requires building strong technical hands-on-skills and experiences as well as establishing close business relationships across the enterprise. Working closely with marketing and sales partners, you will establish and drive long term advance analytics roadmap manage increasing demands for advance analytics in Sales and Marketing. A successful candidate must have a demonstrable background with experience in development of high performance, distributed computing tasks using Big Data technologies and Cloud technologies based on the needs of the organization. Responsible for analyzing, designing, programming, debugging and modifying software enhancements and/or new products used in distributed, large scale analytics solutions.

You will be responsible for:

- Build robust data pipelines using modern data engineering technology stack and Cloud architecture

- Manage application and data integration platforms using AWS Cloud Components

- Understand the business and enable the full life cycle of development/ reporting/ integration projects: planning, design, develop, testing and rollout that confirms to Agile Standards

- Responsible for Data availability/enablement for business reporting within the SLA

- Manage solution providers, define sourcing approach and manage the providers

- Create and manage data, applications and technology architecture documentation and design artifacts

- Work across teams to deliver meaningful reference architectures that outline architecture principles and standard methodologies for technology advancement

- Gain adoption of architecture processes, standards and procedures

- Partner with the Business Analytics team to optimize the cloud / redshift environment to support data sciences capability

- Help maintain the code and capability environment required to evolve data-driven, analytical capabilities with the end goal of understanding customer behavior and competitive dynamics

- Maintains a broad understanding of implementation, integration, and inter-connectivity issues with emerging technologies to define data strategies

- Assist in the decision-making process related to the selection of software architecture solutions

Contribute to the build of the data engineering and feature engineering capabilities required to support customer-centric analytics

- Assist in crafting documents that ensure consistency in development across the online organization.

- Strong experience with writing sophisticated programs, implementing architectures, and enabling automation in these environments

- Consolidate, standardize, and control changes to capacity management data and metric definitions, ownership, accountability, and taxonomy to ensure alignment in understanding

- Collaborate with business domain experts to make strategic recommendations on data management and advocate to improve analytical capability across the organization

- Communicate with various business areas, partner on the formulation of technical requirements for data ingestion, verification, scheduling, etc

 

The experience you bring:

1-2 years of experience in data modeling, data warehousing, and big data architectures

1-2 years of experience of Amazon cloud (AWS)– S3, EC2, RDS, Lambda, DMS, Glue, CloudWatch, SNS etc.

1-2 years of experience in building data pipelines in tools using Apache Airflow

Knowledge of SQL and query optimization techniques

Experience and Databases that include Oracle, MS Sql Server, Postgres, Aurora, Athena, Redshift Spectrum

Experience with Columnar data stores – Redshift/Snowflake/Parquet

Experience working with structured, semi-structured and unstructured data sets

Proficient in application/software architecture (Definition, Business Process Modeling, etc.)

Programming experience with Python or Scala

Knowledge and experience of various data modeling techniques

Knowledge and experience in distributed data processing using Spark or Hadoop

Experience using GitHub, Bit Bucket, or other code repository solution

Understanding of cloud architecture, networking, and security etc.

Understanding and experience working in an agile framework

Experience building traditional data pipelines with Informatica

Exposure to machine learning services from Amazon Web Services (AWS) is a plus

Understanding of RESTful API design/build

DevOps experience is a plus