Streamlining Data Integration You’ll design and automate scalable systems to ingest and transform data from diverse sources, ensuring seamless and efficient data flow across the organization.
Safeguarding Data Quality By implementing robust monitoring frameworks, you’ll proactively detect and resolve data integrity issues, maintaining trust in analytics and reporting.
Establishing Data Governance You’ll lead the development of governance processes to manage metadata, access, and retention, ensuring compliance and secure data usage for internal and external stakeholders.
Building Scalable Data Pipelines You’ll architect reliable and high-performance ETL/ELT pipelines with built-in monitoring and alerts, enabling timely and accurate data delivery for business needs.
Optimizing Database Design and Performance Through thoughtful physical data modeling and indexing strategies, you’ll enhance database efficiency and scalability for large-scale operations.
Modernizing Data Infrastructure You’ll develop and operate advanced storage and processing solutions using distributed and cloud platforms, supporting big data initiatives and analytics.
Automating Data Workflows By leveraging modern tools and techniques, you’ll reduce manual data preparation tasks, boosting productivity and minimizing errors.
Mentoring and Agile Collaboration You’ll coach junior team members and contribute to agile practices like DevOps and Scrum, accelerating delivery of critical analytics projects and fostering team growth.

Requirements

Minimum of 5 years of hands-on experience in data engineering with expertise in Azure Databricks and programming in Scala or Python.
Proven experience in building and maintaining structured streaming pipelines using Spark.
Strong knowledge of big data technologies, including Delta Lake, Apache Spark, Structured Streaming, and SQL.
Experience with Git for version control and CI/CD pipeline management.
Nice to Have (Preferences): Data Engineering Certification (e.g., Databricks Certified Data Engineer, Apache Spark Professional Data Engineer, or equivalent).
Exposure to real-time data ingestion frameworks and cloud-native data services (e.g., Azure Event Hub, Azure Data Lake, AWS SQS, etc).
Familiarity with data governance, access control (e.g., Unity Catalog or Immuta), and performance monitoring tools in cloud environments.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringAzure DatabricksScalaPythonstructured streamingApache SparkSQLETLELTdata modeling

Soft Skills

mentoringagile collaborationcoachingteam growthcommunication

Certifications

Databricks Certified Data EngineerApache Spark Professional Data Engineer