Data Engineer
Q1 Technologies
Location: Chicago-IL - Hybrid 3 days at On-site
Duration: 6 months
Key Responsibilities
• Pipeline Development - Design, implement, and optimize scalable ETL/ELT pipelines using Databricks (PySpark, SQL, Delta Lake) to ingest structured and semi structured data from multiple sources (APIs, databases, streaming).
• Data Warehousing - Build and maintain cloud data warehouse solutions (Snowflake / Azure Synapse) - design star schemas, fact/dimension tables, and aggregate tables for high performance reporting.
• Data Modeling - Create logical and physical data models for operational and analytical use cases; implement SCD Type 2, slowly changing dimensions, and data vault methodologies where appropriate.
• Performance Tuning - Optimize Spark jobs, SQL queries, and data partitioning strategies to handle petabyte scale data with low latency.
• Governance & Quality - Implement data quality checks, monitoring, and lineage using tools like Great Expectations or custom frameworks; enforce data governance policies (GDPR/CCPA).
• Collaboration - Partner with data analysts, product managers, and engineers to translate business requirements into technical data solutions.
• CI/CD & Automation - Automate deployment of data pipelines using Azure DevOps or GitHub Actions; maintain infrastructure as code (Terraform) for data resources.
Required Skills & Experience
• Total Experience: 10+ years in data engineering or related roles.
• Cloud Data Platforms: Deep hands on experience with Databricks (notebooks, jobs, clusters, Delta Lake, Unity Catalog) - must have production level work.
• Data Warehousing: Proven experience with cloud data warehouses (Snowflake, Azure Synapse, or Redshift) - design, optimisation, and administration.
• Data Modeling: Strong knowledge of dimensional modeling (Kimball/Inmon), relational database design, and experience with tools like ER/Studio or dbt.
• Programming: Expert in Python and SQL - ability to write maintainable, production grade code.
• Big Data: Hands on with Apache Spark (PySpark), distributed computing, and performance tuning.
• Orchestration: Experience with workflow tools (Airflow, Azure Data Factory, or Prefect) for scheduling and monitoring pipelines.
• Version Control: Proficient with Git and collaborative development workflows.
Vacancy posted 4 days ago
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