Entry Level Data Engineering Lead Jobs
New grad data engineering lead jobs are open to recent graduates and entry level candidates with zero to two years of experience, where a strong portfolio or internship work can matter more than a long resume. Most openings are on-site, remote, and hybrid roles across Technology & Software, Consulting & Professional Services, and E-Commerce & Online Marketplaces, with employers like Amazon, TikTok, and Capital One hiring at this level now.
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Showing 5 of 640+ Entry Level Data Engineering Lead jobs
INTRODUCTION
For over twenty-seven years RCR Technology Corporation has delivered the highest quality services to our clients with an expansive portfolio of industry-relevant services, and deep technical capabilities around data management and application development.
We are seeking a Senior Azure Data Engineer to help design, build, and operate our next-generation enterprise data platform on Microsoft Azure. You will own end-to-end delivery of data pipelines and data products that power analytics, regulatory reporting, operational dashboards, and emerging AI/ML use cases. You will partner closely with data architects, analytics engineers, data scientists, business stakeholders, and platform engineering teams to deliver reliable, performance, secure, and cost-efficient data solutions.
This role is ideal for an engineer with strong hands-on depth in Azure Data Factory, Azure Synapse Analytics and/or Databricks, and modern Lakehouse patterns, who is comfortable leading migration programs (e.g., Informatica-to-ADF, on-prem warehouse-to-cloud), mentoring mid-level engineers, and shaping engineering standards across the team.
KEY RESPONSIBILITIES
Pipeline Design & Development:
- Design and build robust, reusable, parameter-driven ingestion and transformation pipelines using Azure Data Factory, Synapse Pipelines, Data Bricks and/or Microsoft Fabric Data Factory.
- Implement medallion architecture (Bronze / Silver / Gold) on Azure Data Lake Storage Gen2 using Delta Lake, Parquet, and structured streaming patterns.
- Build performant ELT workflows that leverage pushdown to source systems (Synapse Dedicated SQL Pool, Azure SQL, Teradata) where appropriate.
- Develop and optimize PySpark notebooks and jobs on Azure Databricks or Synapse Spark.
Data Modeling & Warehousing:
- Design dimensional models (Kimball star/snowflake) and data vault patterns for analytics consumption.
- Implement Slowly Changing Dimensions (Type 1/2/3), Change Data Capture, and late-arriving data patterns.
- Tune distributed SQL workloads in Synapse Dedicated SQL Pool / Fabric Warehouse, including distribution keys, partitioning, and clustered column store indexes.
Platform Engineering & DevOps:
- Implement CI/CD for data pipelines using Azure DevOps (YAML pipelines, ARM/Bicep/Terraform) across Dev / SIT / UAT / Prod environments.
- Instrument pipelines with robust logging, auditing, and monitoring using Azure Monitor, Log Analytics, and KQL.
- Define and enforce coding standards, code review practices, branching strategies, and release management.
Migration & Modernization:
- Lead or contribute to legacy-to-cloud migrations — e.g., Informatica PowerCenter to Azure Data Factory, on-premises Teradata / Oracle / SQL Server to Synapse or Fabric.
- Perform workload assessment, capacity planning, and cost modeling for target-state architectures.
- Production incident response for critical pipelines.
REQUIRED QUALIFICATIONS
- Deep hands-on expertise with Azure Data Factory: pipelines, datasets, linked services, triggers, parameterization, mapping data flows, and all three Integration Runtime types (Azure, Self-hosted, SSIS).
- Strong Experience in Data Bricks and PySpark.
- Production experience with one or more of: Azure Synapse Analytics (Dedicated and Serverless SQL Pools, Spark Pools) OR Azure Databricks (Delta Lake, Unity Catalog) OR Microsoft Fabric (Warehouse, Lakehouse, OneLake).
- Strong working knowledge of Azure Data Lake Storage Gen2 (hierarchical namespace, RBAC + ACLs, lifecycle management, security).
- Experience with Azure Key Vault, Azure AD / Entra ID (including managed identities and service principals), and private networking (VNet integration, private endpoints).
- Monitoring and troubleshooting with Azure Monitor, Log Analytics, and KQL.
- Advanced SQL — window functions, CTEs, query optimization, execution plan analysis, performance tuning.
- Strong Python for data engineering — pandas, PySpark, REST API integration, unit testing (pytest).
- Proficient in T-SQL; familiarity with Spark SQL, KQL, PowerShell, and Bash shell scripting.
PREFERRED QUALIFICATIONS
- 5+ years of data warehouse development experience.
- 5+ years of data modeling experience using ERWIN or similar tools.
- 2+ years of experience with Azure Data Factory and Snowflake.
- Medicaid Domain Knowledge is a plus.
INTRODUCTION
For over twenty-seven years RCR Technology Corporation has delivered the highest quality services to our clients with an expansive portfolio of industry-relevant services, and deep technical capabilities around data management and application development.
We are seeking a Senior Azure Data Engineer to help design, build, and operate our next-generation enterprise data platform on Microsoft Azure. You will own end-to-end delivery of data pipelines and data products that power analytics, regulatory reporting, operational dashboards, and emerging AI/ML use cases. You will partner closely with data architects, analytics engineers, data scientists, business stakeholders, and platform engineering teams to deliver reliable, performance, secure, and cost-efficient data solutions.
This role is ideal for an engineer with strong hands-on depth in Azure Data Factory, Azure Synapse Analytics and/or Databricks, and modern Lakehouse patterns, who is comfortable leading migration programs (e.g., Informatica-to-ADF, on-prem warehouse-to-cloud), mentoring mid-level engineers, and shaping engineering standards across the team.
KEY RESPONSIBILITIES
Pipeline Design & Development:
- Design and build robust, reusable, parameter-driven ingestion and transformation pipelines using Azure Data Factory, Synapse Pipelines, Data Bricks and/or Microsoft Fabric Data Factory.
- Implement medallion architecture (Bronze / Silver / Gold) on Azure Data Lake Storage Gen2 using Delta Lake, Parquet, and structured streaming patterns.
- Build performant ELT workflows that leverage pushdown to source systems (Synapse Dedicated SQL Pool, Azure SQL, Teradata) where appropriate.
- Develop and optimize PySpark notebooks and jobs on Azure Databricks or Synapse Spark.
Data Modeling & Warehousing:
- Design dimensional models (Kimball star/snowflake) and data vault patterns for analytics consumption.
- Implement Slowly Changing Dimensions (Type 1/2/3), Change Data Capture, and late-arriving data patterns.
- Tune distributed SQL workloads in Synapse Dedicated SQL Pool / Fabric Warehouse, including distribution keys, partitioning, and clustered column store indexes.
Platform Engineering & DevOps:
- Implement CI/CD for data pipelines using Azure DevOps (YAML pipelines, ARM/Bicep/Terraform) across Dev / SIT / UAT / Prod environments.
- Instrument pipelines with robust logging, auditing, and monitoring using Azure Monitor, Log Analytics, and KQL.
- Define and enforce coding standards, code review practices, branching strategies, and release management.
Migration & Modernization:
- Lead or contribute to legacy-to-cloud migrations — e.g., Informatica PowerCenter to Azure Data Factory, on-premises Teradata / Oracle / SQL Server to Synapse or Fabric.
- Perform workload assessment, capacity planning, and cost modeling for target-state architectures.
- Production incident response for critical pipelines.
REQUIRED QUALIFICATIONS
- Deep hands-on expertise with Azure Data Factory: pipelines, datasets, linked services, triggers, parameterization, mapping data flows, and all three Integration Runtime types (Azure, Self-hosted, SSIS).
- Strong Experience in Data Bricks and PySpark.
- Production experience with one or more of: Azure Synapse Analytics (Dedicated and Serverless SQL Pools, Spark Pools) OR Azure Databricks (Delta Lake, Unity Catalog) OR Microsoft Fabric (Warehouse, Lakehouse, OneLake).
- Strong working knowledge of Azure Data Lake Storage Gen2 (hierarchical namespace, RBAC + ACLs, lifecycle management, security).
- Experience with Azure Key Vault, Azure AD / Entra ID (including managed identities and service principals), and private networking (VNet integration, private endpoints).
- Monitoring and troubleshooting with Azure Monitor, Log Analytics, and KQL.
- Advanced SQL — window functions, CTEs, query optimization, execution plan analysis, performance tuning.
- Strong Python for data engineering — pandas, PySpark, REST API integration, unit testing (pytest).
- Proficient in T-SQL; familiarity with Spark SQL, KQL, PowerShell, and Bash shell scripting.
PREFERRED QUALIFICATIONS
- 5+ years of data warehouse development experience.
- 5+ years of data modeling experience using ERWIN or similar tools.
- 2+ years of experience with Azure Data Factory and Snowflake.
- Medicaid Domain Knowledge is a plus.
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Find JobsEntry Level Data Engineering Lead Job Market
Who's Hiring
- Amazon95
- TikTok22
- Capital One15
- CBRE14
- Medpace12
Top Industries Hiring
- Technology & Software226
- Consulting & Professional Services53
- E-Commerce & Online Marketplaces45
- Retail44
- Healthcare & Medical Services43
Entry Level Data Engineering Lead Jobs: Frequently Asked Questions
How do I get an entry level data engineering lead job?
Build a portfolio that demonstrates hands-on skills: data pipeline projects, ETL work, and experience with tools like Python, SQL, Spark, or Airflow will get attention at this stage. Internships and academic capstone projects count as real experience. Employers hiring at the entry level typically care more about what you have built and your foundational understanding of data architecture than the number of years on your resume.
Which companies hire entry level data engineering leads?
Companies hiring entry level data engineering leads right now include Amazon, TikTok, and Capital One, based on current listings on Migrate Mate as of November 2026. At this experience level, you will find a mix of large tech employers, fast-growing startups, and data-intensive enterprises in finance and healthcare that actively recruit candidates early in their careers.
Are there remote entry level data engineering lead jobs?
Yes, though on-site and hybrid roles are still common at the entry level. About 28% of entry level data engineering lead openings are remote or hybrid as of November 2026, so candidates are not limited by geography. Filtering by work setting on Migrate Mate lets you quickly see which of the current openings match your preference.
Are these new grad data engineering lead jobs?
Yes, this page includes new grad, recent graduate, and junior data engineering lead roles. A posting is new-grad friendly when it welcomes zero to two years of experience, accepts internship or project work in place of full-time history, or explicitly invites candidates who are early in their careers. If you are a new grad or recent graduate, those are the openings to prioritize in your search.
Which industries hire the most entry level data engineering leads?
Entry Level data engineering lead roles concentrate in Technology & Software, Consulting & Professional Services, and E-Commerce & Online Marketplaces, based on current listings on Migrate Mate as of November 2026. These sectors generate large volumes of structured and unstructured data, which drives consistent demand for early-career engineers who can build and maintain reliable data infrastructure from the ground up.