Data Engineer

About The Role

We're looking for a Data Engineer to build and maintain the cloud data platforms that power analytics, BI, and AI/ML for our clients. You'll move raw, fragmented data into clean, reliable, query-ready form on Snowflake and Databricks — supporting everything from customer-360 dashboards to real-time decision-making.

Key Responsibilities

• Build, test, and maintain ELT/ETL pipelines using Matillion, Fivetran, and Azure Data Factory (ADF) to ingest data from databases, APIs, files, and SaaS sources.
• Develop data transformations and workloads on Snowflake and Databricks.
• Develop and maintain SSIS ETL packages and jobs for data integration and scheduled workloads.
• Configure and manage Fivetran connectors, including Salesforce integration into the data warehouse.
• Develop and optimize SQL transformations and data models for analytics and reporting.
• Work with AWS, including managing data in S3 (staging, ingestion, and storage).
• Write clean, reusable Python for data processing, automation, and pipeline logic.
• Support data quality, validation, and monitoring for downstream BI (Power BI/Tableau) and ML teams.
• Track and manage work through Jira workflows; partner with analysts and client stakeholders on requirements.
• Document pipelines, data definitions, and processes.
  
  

Must-Have Skills

• 1–3 years of hands-on data engineering experience.
• Azure Data Factory (ADF) — building and orchestrating pipelines.
• Databricks — hands-on development of transformations/workloads.
• Matillion — building ELT pipelines.
• Fivetran — configuring and managing connectors.
• Snowflake — hands-on data modeling and transformation.
• Strong SQL and solid working knowledge of Python.
• Understanding of data warehousing concepts, ETL/ELT, and dimensional modeling.
  
  

Good to Have

• Experience with SSIS packages and jobs.
• Salesforce data integration via Fivetran.
• AWS S3 experience.
• Exposure to Redshift or Synapse.
• BI tools (Power BI, Tableau).
• Familiarity with Jira workflows.
• Exposure to data for AI/ML workflows.
  
  

What We Look For

Curiosity, ownership, and a problem-solving mindset; comfort in a consulting/client-delivery environment; clear communication and a willingness to learn fast.