FIND_THE_RIGHTJOB.
Associate III - Data Engineering
JOB_REQUIREMENTS
Hires in
Not specified
Employment Type
Not specified
Company Location
Not specified
Salary
Not specified
Role description
Role Proficiency:
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:
- Collaborate closely with data analysts data scientists and other stakeholders to ensure data accessibility quality and security across various data sources.rnDesign develop and maintain data pipelines that collect process and transform large volumes of data from various sources.
- Implement ETL (Extract Transform Load) processes to facilitate efficient data movement and transformation.
- Integrate data from multiple sources including databases APIs cloud services and third-party data providers.
- Establish data quality checks and validation procedures to ensure data accuracy completeness and consistency.
- 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:
- Adherence to engineering processes and standards
- Adherence to schedule / timelines
- Adhere to SLAs where applicable
- # of defects post delivery
- # of non-compliance issues
- Reduction of reoccurrence of known defects
- Quickly turnaround production bugs
- Completion of applicable technical/domain certifications
- Completion of all mandatory training requirementst
- Efficiency improvements in data pipelines (e.g. reduced resource consumption faster run times).
- Average time to detect respond to and resolve pipeline failures or data issues.
Outputs Expected:
Code Development:
- Develop data processing code independently
ensuring it meets performance and scalability requirements.
Documentation:
- Create documentation for personal work and review deliverable documents
including source-target mappings
test cases
and results.
Configuration:
- Follow configuration processes diligently.
Testing:
- Create and conduct unit tests for data pipelines and transformations to ensure data quality and correctness.
- Validate the accuracy and performance of data processes.
Domain Relevance:
- Develop features and components with a solid understanding of the business problems being addressed for the client.
- Understand data schemas in relation to domain-specific contexts
such as EDI formats.
Defect Management:
- Raise
fix
and retest defects in accordance with project standards.
Estimation:
- Estimate time
effort
and resource dependencies for personal work.
Knowledge Management:
- Consume and contribute to project-related documents
SharePoint
libraries
and client universities.
Design Understanding:
- Understand design and low-level design (LLD) and link it to requirements and user stories.
Certifications:
- Obtain relevant technology certifications to enhance skills and knowledge.
Skill Examples:
- Proficiency in SQL Python or other programming languages utilized for data manipulation.
- Experience with ETL tools such as Apache Airflow Talend Informatica AWS Glue Dataproc and Azure ADF.
- Hands-on experience with cloud platforms like AWS Azure or Google Cloud particularly with data-related services (e.g. AWS Glue BigQuery).
- Conduct tests on data pipelines and evaluate results against data quality and performance specifications.
- Experience in performance tuning data processes.
- Proficiency in querying data warehouses.
Knowledge Examples:
Knowledge Examples
- Knowledge of various ETL services provided by cloud providers including Apache PySpark AWS Glue GCP DataProc/DataFlow and Azure ADF/ADLF.
- Understanding of data warehousing principles and practices.
- Proficiency in SQL for analytics including windowing functions.
- Familiarity with data schemas and models.
- Understanding of domain-related data and its implications.
Additional Comments:
Job Title: Data Services Engineer – AWS & Snowflake Job Summary: As a Data Services Engineer, you will be responsible for designing, developing, and maintaining robust data solutions using AWS cloud services and Snowflake. You will work closely with cross-functional teams to ensure data is accessible, secure, and optimized for performance. Your role will involve implementing scalable data pipelines, managing data integration, and supporting analytics initiatives. Responsibilities: • Design and implement scalable and secure data pipelines on AWS and Snowflake (Star/Snowflake schema) • Optimize query performance using clustering keys, materialized views, and caching • Develop and maintain Snowflake data warehouses and data marts. • Build and maintain ETL/ELT workflows using Snowflake-native features (Snowpipe, Streams, Tasks). • Integrate Snowflake with cloud platforms (AWS, Azure, GCP) and third-party tools (Airflow, dbt, Informatica) • Utilize Snowpark and Python/Java for complex transformations • Implement RBAC, data masking, and row-level security. • Optimize data storage and retrieval for performance and cost-efficiency. • Collaborate with stakeholders to gather data requirements and deliver solutions. • Ensure data quality, governance, and compliance with industry standards. • Monitor, troubleshoot, and resolve data pipeline and performance issues. • Document data architecture, processes, and best practices. • Support data migration and integration from various sources. Qualifications: • Bachelor’s degree in Computer Science, Information Technology, or a related field. • 3 to 4 years of hands-on experience in data engineering or data services. • Proven experience with AWS data services (e.g., S3, Glue, Redshift, Lambda). • Strong expertise in Snowflake architecture, development, and optimization. • Proficiency in SQL and Python for data manipulation and scripting. • Solid understanding of ETL/ELT processes and data modeling. • Experience with data integration tools and orchestration frameworks. • Excellent analytical, problem-solving, and communication skills. Preferred Skills: • AWS Glue, AWS Lambda, Amazon Redshift • Snowflake Data Warehouse • SQL & Python
Skills
Aws Lambda,AWS Glue,Amazon Redshift,Snowflake Data Warehouse
About UST
Similar jobs
No similar jobs found
© 2025 Qureos. All rights reserved.