Associate III

This role requires proficiency in data pipeline development including coding and testing data pipelines for ingesting wrangling transforming and joining data from various sources. Must be adept at using ETL tools such as Informatica Glue Databricks and DataProc with coding skills in Python PySpark and SQL. Works independently and demonstrates proficiency in at least one domain related to data with a solid understanding of SCD concepts and data warehousing principles.

Outcomes:

1. Collaborate closely with data analysts data scientists and other stakeholders to ensure data accessibility quality and security across various data sources. Design develop and maintain data pipelines that collect process and transform large volumes of data from various sources.
2. Implement ETL (Extract Transform Load) processes to facilitate efficient data movement and transformation.
3. Integrate data from multiple sources including databases APIs cloud services and third-party data providers.
4. Establish data quality checks and validation procedures to ensure data accuracy completeness and consistency.
5. Develop and manage data storage solutions including relational databases NoSQL databases and data lakes. Stay updated on the latest trends and best practices in data engineering cloud technologies and big data tools.

Measures of Outcomes:

1. Adherence to engineering processes and standards
2. Adherence to schedule / timelines
3. Adhere to SLAs where applicable
4. # of defects post delivery
5. # of non-compliance issues
6. Reduction of reoccurrence of known defects
7. Quickly turnaround production bugs
8. Completion of applicable technical/domain certifications
9. Completion of all mandatory training requirements
10. Efficiency improvements in data pipelines (e. g. reduced resource consumption faster run times).
11. Average time to detect respond to and resolve pipeline failures or data issues.

Outputs Expected:

Code Development:

1. Develop data processing code independently
   ensuring it meets performance and scalability requirements.

Documentation:

1. Create documentation for personal work and review deliverable documents
   including source-target mappings
   test cases
   and results.

Configuration:

1. Follow configuration processes diligently.

Testing:

1. Create and conduct unit tests for data pipelines and transformations to ensure data quality and correctness.
2. Validate the accuracy and performance of data processes.

Domain Relevance:

1. Develop features and components with a solid understanding of the business problems being addressed for the client.
2. Understand data schemas in relation to domain-specific contexts
   such as EDI formats.

Defect Management:

1. Raise
   fix
   and retest defects in accordance with project standards.

Estimation:

1. Estimate time
   effort
   and resource dependencies for personal work.

Knowledge Management:

1. Consume and contribute to project-related documents
   SharePoint
   libraries
   and client universities.

Design Understanding:

1. Understand design and low-level design (LLD) and link it to requirements and user stories.

Certifications:

1. Obtain relevant technology certifications to enhance skills and knowledge.

Skill Examples:

1. Proficiency in SQL Python or other programming languages utilized for data manipulation.
2. Experience with ETL tools such as Apache Airflow Talend Informatica AWS Glue Dataproc and Azure ADF.
3. Hands-on experience with cloud platforms like AWS Azure or Google Cloud particularly with data-related services (e. g. AWS Glue BigQuery).
4. Conduct tests on data pipelines and evaluate results against data quality and performance specifications.
5. Experience in performance tuning data processes.
6. Proficiency in querying data warehouses.

Knowledge Examples:

Knowledge Examples

1. Knowledge of various ETL services provided by cloud providers including Apache PySpark AWS Glue GCP DataProc/DataFlow and Azure ADF/ADLF.
2. Understanding of data warehousing principles and practices.
3. Proficiency in SQL for analytics including windowing functions.
4. Familiarity with data schemas and models.
5. Understanding of domain-related data and its implications.

Data and Analytics - Oracle/database developer Qualifications: 5-7 years industry experience Technical skills requirement: Must have: Minimum 5 years of experience developing ETL Solutions using Apache Spark and Scala/Python Strong proficiency in Oracle SQL/PL SQL Knowledge of real-time data processing frameworks like Kafka Familiarity with CI/CD pipelines and DevOps practices Good to have: Experience using Agile methodologies Experience on Banking and Capital Markets domains Other Key capabilities: Client facing skills and proven ability in effective planning, executing and problem-solving Excellent communication, inter-personal, and teamworking skills Multi-tasking attitude, flexible with ability to change priorities quickly Methodical approach, logical thinking, and ability to plan work and meet deadlines Accuracy and attention to details Written and verbal communication skills Ability to quickly understand and learn new technology/feature and should inspire change to make an impact within the team and in a client organization