Roles and Responsibilities

Essential:

• 5-8 years of work experience in data management disciplines including [data

integration, modeling, optimization and data quality], and/or other areas directly

relevant to data engineering responsibilities and tasks.

• At least three years of experience working in cross-functional teams and

collaborating with business stakeholders in support of a departmental and/or

multi-departmental data management and analytics initiative.

• Strong experience with advanced analytics tools for Object-oriented/object

function scripting using languages such as [R, Python, Java, C++, Scala,

others].

• Strong ability to design, build and manage data pipelines for data structures

encompassing data transformation, data models, schemas, metadata and

workload management. The ability to work with both IT and business in

integrating analytics and data science output into business processes and

workflows.

• Strong experience with popular database programming languages including

[SQL, PL/SQL, others] for relational databases and certifications on upcoming

[NoSQL/Hadoop oriented databases like MongoDB, Cassandra, others] for

non-relational databases.

• Strong experience in working with large, heterogeneous datasets in building and

optimizing data pipelines, pipeline architectures and integrated datasets using

traditional data integration technologies. These should include [ETL/ELT, data

replication/CDC, message-oriented data movement, API design and access]

and upcoming data ingestion and integration technologies such as [stream data

integration, CEP and data virtualization].

• Strong experience in working with SQL on Hadoop tools and technologies

including [HIVE, Impala, Presto, others] from an open source perspective and

[Hortonworks Data Flow (HDF), Dremio, Informatica, Talend, others] from a

commercial vendor perspective.

• Basic experience in working with [data governance/data quality] and [data

security] teams and specifically [information stewards] and [privacy and security

officers] in moving data pipelines into production with appropriate data quality,

governance and security standards and certification.