ETL Developer w. AWS
- Location: Denver, CO (Onsite)
- # of Positions: 1
- Eligibility: Open
- Client Name / Domain: Fortune 1000
- Bill Rate: $?? per hour
- Employment Mode: Contract / Corp-to-Corp
- Contract Duration: 12+ mos. Contract
- Experience: 8+ years
- Skills: ETL, AWS, RedShift, Apache Airflow, Snowflake, DWH, Control-M / Autosys, SQL, Shell Scripts, PL/SQL, Google Cloud, Python, PySpark, Spark
The Ask? Client is looking for a ETL AWS Consultant with experience in designing, building, and maintaining data pipelines using AWS services to extract, transform, and load (ETL) data from various sources into a data warehouse or data lake, requiring expertise in AWS technologies like Glue, S3, Redshift, etc.,
Skills and Experience_
- Knowledge of building data ingestion pipelines both real time and batch using best practices
- Strong Knowledge of writing SQL, PL/SQL
- Experience with Cloud Computing platforms like Amazon AWS, Google Cloud etc.
- Experience in working cloud data warehouse like Redshift / Snowflake
- Proficient in writing Shell Scripts or Python scripts
- Experience with relational SQL and NoSQL databases, including Postgres, and Mongodb.
- Strong interpersonal, analytical, problem-solving, influencing, prioritization, decision- making and conflict resolution skills
- Experience in scheduling tools like Control-M or Autosys
- Strong debugging skills on complex SQLs and analyzing the data
- Excellent written/verbal communicaton skills.
Responsibilities_
- Data Transformation and Modeling:
- Design and implement data transformations and models using dbt to create structured, cleaned, and aggregated datasets.
- Develop and maintain dbt models that accurately represent business logic and data requirements.
- SQL Expertise:
- Write and optimize SQL queries within dbt to extract, manipulate, and join data from various sources (e.g., databases, APIs, flat files).
- Ensure SQL code follows best practices for readability, performance, and maintainability.
- Version Control:
- Use version control systems (e.g., Git) to manage dbt codebase, enabling collaborative development and tracking changes over time.
- Collaborate with data engineers and analysts to coordinate code changes.
- Testing and Documentation:
- Implement unit tests within dbt to verify the accuracy and reliability of data transformations.
- Document dbt models, data lineage, and transformations to facilitate understanding and collaboration.
- Automation:
- Schedule and automate dbt runs to keep data models up-to-date and synchronized with source systems.
- Implement data orchestration and scheduling as needed.
- Data Quality Assurance:
- Develop and enforce data quality checks and validations within dbt to identify and rectify data issues.
- Monitor data quality and integrity, responding to anomalies or discrepancies.
- Performance Optimization:
- Optimize dbt models and queries for performance, identifying and addressing bottlenecks.
- Analyze and fine-tune data processing pipelines to meet performance requirements.
- Collaboration:
- Collaborate closely with data engineers, data analysts, and business stakeholders to understand data requirements and deliver data solutions.
- Participate in cross-functional teams and contribute to data-related projects.
RESPONDENTS [ please read carefully ]
U.S. Citizens and those authorized to work in the U.S. are encouraged to apply. We are unable to sponsor at this time. Any equivocation or misrepresentation will be reported to USCIS and BBB.
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