Data Implementation Engineer

4+ years of Data Engineering experience Advanced SQL; experience with relational and NoSQL databases Strong experience with Snowflake, Hadoop, Spark, PySpark, Databricks, Azure Proficiency in Python for data engineering Familiarity with orchestration tools and workflow management systems Responsibilities: Design, build, and maintain scalable data pipelines using Snowflake, Hadoop, Spark, PySpark, Azure Implement data architectures and optimize workflows for large financial datasets Write high-quality, maintainable Python and SQL code following best practices Integrate data governance, metadata management, and lineage tracking into solution

The Data Implementation Engineer will design, build, and maintain scalable data pipelines using various technologies. They will also implement data architectures and optimize workflows for large financial datasets.

Candidates should have over 4 years of experience in Data Engineering and proficiency in SQL and Python. Familiarity with orchestration tools and experience with Snowflake, Hadoop, Spark, and Azure is also required.

Identified from the job description. Confirm important requirements above.

• Data Engineering
• SQL
• Relational Databases
• NoSQL
• Snowflake
• Hadoop
• Spark
• PySpark
• Databricks
• Azure
• Python
• Data Governance
• Metadata Management
• Lineage Tracking
• Workflow Management
• Data Pipelines

• Technology
• Data & Analytics
• Software
• Engineering
• Consulting

Minimum experience

5+ years

Apply by

Jan 9, 2027

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level

Application method

Direct apply is available