DATA ENGINEER

Numeric Technologies, Inc is looking for a Job Title: DATA ENGINEER to provide services for our
client:

Job Duties:
Cloud Data Platform Engineering and Migration:
Led the design, development, and implementation of cloud-based data platforms using
technologies such as Snowflake and AWS, ensuring scalable and efficient data solutions.
Managed seamless transitions of data infrastructure from on-premises systems like SQL
Server to cloud environments like Snowflake, improving performance by up to 70%
through optimized SQL code and view logic migration.
Developed comprehensive ETL strategies using Matillion and Airflow, creating advanced
orchestration and transformation jobs tailored for Snowflake. Enhanced data processing
efficiency and scalability.
Established data governance frameworks and best practices to ensure data quality,
security, and compliance, including the implementation of data standards and master data
management practices.
Designed and implemented scalable data models and data architecture to support diverse
business requirements, optimizing storage and retrieval for large datasets.
Conducted detailed assessments and planning for cloud migration projects, including
cost-benefit analysis, risk assessment, and defining migration roadmaps to ensure smooth
transitions with minimal disruption.
Implemented data security measures, including encryption, access controls, and auditing,
to ensure compliance with regulatory requirements and protect sensitive data during and
after migration.
Data Pipeline Management & Enhancements:
Enhanced data processing efficiency by 40% through the use of advanced DBT features,
developing complex DBT models for automated data cleansing, transformation, and
aggregation processes.
Innovated within Snowflake by developing Snowpark stored procedures for direct API
calls, reducing reliance on traditional ETL tools and operational costs. Implemented
procedures for logging, exception handling, and secure password management.
Implemented comprehensive monitoring, logging, and alerting systems using Airflow
and AWS services to ensure the reliability and performance of data pipelines. Enabled
real-time data processing and error detection.

Developed and managed end-to-end ETL pipelines using Matillion, Airflow, and Python,
ensuring efficient data flow from source to target systems.
Optimized ETL operations by incorporating partitioning and indexing strategies, which
resulted in a 50% performance boost for data processing tasks.
Integrated data from various sources including SAP Hana, SQL Server, and cloud storage
into Snowflake, ensuring seamless data consolidation and availability for analytics.
Automated data validation processes to maintain data integrity and accuracy across
different stages of the pipeline, using custom scripts and automated testing frameworks.
Streamlined the data transformation process by creating reusable data transformation
templates and libraries, reducing development time and improving consistency across
projects.
Implemented real-time data ingestion and processing solutions using AWS Glue and
Kinesis, enabling the organization to make timely data-driven decisions.
Led the migration of ETL workflows from legacy systems like SSIS to modern
orchestration tools such as Airflow and Matillion, modernizing data pipeline management
and improving job efficiency by 50%.
Created robust error handling and retry mechanisms within ETL pipelines to minimize
data loss and ensure smooth operation, even in the event of partial failures.
Conducted regular performance tuning and optimization of SQL queries and data
transformations to reduce execution time and resource consumption.
Developed and maintained detailed documentation for all ETL processes, including data
flow diagrams, transformation logic, and troubleshooting guides, ensuring easy
knowledge transfer and maintenance.

Machine Learning Models Utilization:
Built and operationalized ML workflows and pipelines, collaborating with data scientists
to streamline the model development lifecycle. Ensured robust data preprocessing and
feature engineering for improved model accuracy.
Implemented monitoring, logging, and alerting systems to ensure the reliability and
performance of ML models in production environments, using tools such as AWS
CloudWatch and custom monitoring scripts.

Data Product Management:
Developed strategic vision and roadmaps for data-driven products and solutions aligned
with business objectives. Prioritized features based on market trends, customer needs, and
competitive analysis to drive product innovation.
Owned the end-to-end lifecycle of data products from ideation to continuous
improvement. Managed product backlogs, led cross-functional teams for successful
launches, and implemented data-driven strategies to drive user engagement and retention.
Identified opportunities to monetize data assets and developed robust business cases
articulating financial and operational benefits. Implemented strategies to leverage data as
a strategic asset.
Agile Transformation and Delivery Leadership:
Spearheaded the adoption of agile methodologies and DevOps practices across the
organization. Established high-performing, multi-geographic teams and fostered a culture
of continuous improvement through regular sprint planning, retrospectives, and agile
training sessions.
Established agile frameworks, processes, and ceremonies tailored to the organization's
unique needs. Coached and mentored cross-functional teams on agile principles and
practices, using tools like JIRA and Confluence to manage workflows and
documentation.
Implemented CI/CD pipelines to automate the build, test, and deployment processes,
improving development efficiency and reducing time to market.

Data Governance and Security:
Led the implementation of data governance policies and practices to ensure data quality,
security, and compliance. Established data standards, maintained data quality, and
implemented master data management practices.
Developed strategies to leverage data as a strategic asset while ensuring robust security
measures to protect sensitive information. Implemented data encryption, access controls,
and compliance with regulatory requirements.
Identified, assessed, and mitigated technology-related risks, including cybersecurity,
regulatory, and operational risks. Established incident response and business continuity
plans to ensure the resilience of critical systems and applications.

Degree requirement: Bachelors degree in computer science, computer information systems,
information technology, related engineering (computer or software) or a combination of education and
experience equating to the U.S. equivalent of a bachelor s degree in one of the aforementioned
subjects.

Please submit resumes to