Data Engineer

Hi,

Please go through the job description:

Title: Data Engineer

Location: Austin, TX(Hybrid Local)

Duration: 6+ months

Job Description:

The response to this request must include all relevant SDLC artifacts for the project, as discussed with the manager.
In addition to the development of the software, and corresponding artifacts, the scope must include participation and support of reviews with client internal groups, testing at various stages of the project, and production cutover and warranty support.

Additional Comments:
We are looking for a data analytics engineer (a data engineer who specializes in the data analytics space) with the following expertise:

Core Professional Competencies
1. Communication: Clearly communicates technical work to diverse audiences, verbally and in writing. Participate in peer reviews and team discussions with clarity and purpose.
2. Documentation: Maintains clear, structured documentation of project logic, decisions, and maintenance. Contributes to team standards for reproducibility and transparency.
3. Collaboration: Works effectively with cross-functional partners. Values shared ownership and ensured continuity through knowledge sharing.
4. Initiative: Comfortable in ambiguity; proactively identifies issues and opportunities. Demonstrates curiosity and critical thinking.
5. Attention to Detail: Delivers high-quality, consistent code and documentation that supports long-term maintainability and trust in data systems.

Data Engineering Expertise (5+ years)
Experienced in building and maintaining data pipelines (ETL/ELT)
Proficient with orchestration tools (e.g., Airflow, dbt, Prefect)
Comfortable working with cloud platforms (e.g., AWS) and tools like Snowflake
Familiar with data lake and warehouse architecture (e.g., S3+ Athena, Delta Lake)
5. Strong Python skills for data manipulation (e.g., pandas, pyarrow, pyspark)

Data Infrastructure & Management (5+ years)
Expertise in data modeling (star/snowflake schemas, normalization, dimensional modeling)
Skilled in maintaining data quality and integrity (data monitoring, validation, deduplication, anomaly detection)
Familiar with version control, CI/CD practices for data workflows (e.g., Git)