Entry Level Data Engineering Consultant Jobs
New grad data engineering consultant jobs welcome 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 reflect a mix of on-site, remote, and hybrid settings across Technology & Software, Consulting & Professional Services, and E-Commerce & Online Marketplaces, with employers like Amazon, TikTok, and Capital One hiring at this level now.
Find JobsOverview
Showing 5 of 659+ Entry Level Data Engineering Consultant 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.
See All 659+ Entry Level Data Engineering Consultant Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsEntry Level Data Engineering Consultant Job Market
Who's Hiring
- Amazon95
- TikTok22
- Capital One15
- CBRE14
- Medpace12
Top Industries Hiring
- Technology & Software229
- Consulting & Professional Services67
- E-Commerce & Online Marketplaces45
- Retail44
- Healthcare & Medical Services43
Entry Level Data Engineering Consultant Jobs: Frequently Asked Questions
How do I get an entry level data engineering consultant job?
Employers hiring at this level look for hands-on proof of skill over years of experience. Building a portfolio that includes real data pipeline projects, ETL work, or cloud-platform exercises gives you a concrete edge. Familiarity with SQL, Python, and at least one cloud environment is common across job postings. Internship experience, bootcamp projects, or open-source contributions all count as relevant work at this stage.
Which companies hire entry level data engineering consultants?
Companies hiring entry level data engineering consultants right now include Amazon, TikTok, and Capital One, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from consulting firms, technology service providers, and large enterprises building out analytics teams who train junior engineers on the job.
Are there remote entry level data engineering consultant jobs?
Yes, though the mix varies by employer and project type. About 29% of entry level data engineering consultant openings are remote or hybrid as of November 2026, reflecting strong demand for flexible roles in this field. On-site positions still appear frequently, particularly at consulting firms where client collaboration is part of the work.
Are these new grad data engineering consultant jobs?
Yes, this page includes new grad, recent graduate, and junior data engineering consultant roles alongside other entry level openings. A posting is typically new-grad friendly when it welcomes zero to two years of experience, accepts internships or project work in place of professional history, or lists a portfolio as a qualifying credential. Look for language like "entry level," "junior," or "new grad" in the job description to confirm.
Which industries hire the most entry level data engineering consultants?
Entry Level data engineering consultant 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 drive hiring at this level because they handle large, complex data at scale and rely on junior engineers to build and maintain the pipelines that support analytics and business operations.