Data Engineer designing and building data pipelines at LexisNexis. Collaborating with partners for high - quality analytics and AI - ready data.
About the role
- Senior Data Engineer leading development and optimization of data solutions. Managing complex projects, ensuring data quality, and mentoring junior engineers in data engineering.
Responsibilities
- Lead design and optimization of complex data solutions.
- Oversee data engineering projects.
- Mentor junior engineers.
- Ensure solutions align with organizational goals.
- Provide strategic guidance.
Requirements
- 10+ years in data engineering/ETL roles, with at least 6+ years in Azure cloud ETL leadership.
- Azure certifications (e.g., Azure Analytics Specialty, Solutions)
- Azure Data Sources: Azure Data Lake Storage (ADLS), Blob Storage, Azure SQL Database, Synapse Analytics.
- External Sources: APIs, on-prem databases, flat files (CSV, Parquet, JSON).
- Tools: Azure Data Factory (ADF) for orchestration, Databricks connectors.
- Apache Spark: Strong knowledge of Spark (PySpark, Spark SQL) for distributed processing.
- Data Cleaning & Normalization: Handling nulls, duplicates, schema evolution.
- Performance Optimization: Partitioning, caching, broadcast joins.
- Delta Lake: Implementing ACID transactions, time travel, and schema enforcement.
- Azure Data Factory (ADF): Building pipelines to orchestrate Databricks notebooks.
- Azure Key Vault: Secure credential management.
- Azure Monitor & Logging: For ETL job monitoring and alerting.
- Networking & Security: VNET integration, private endpoints.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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