Data Integration Engineer Jobs
Data Integration Engineer jobs are open across healthcare, fintech, retail, and enterprise tech, from junior to staff and principal levels, with specializations in ETL pipeline development, cloud data platforms, and API integration. Find a role that fits from the openings below and apply directly.
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Showing 5 of 175+ Data Integration Engineer jobs
Key Qualifications
MUST HAVE:
-
Snowflake Data Engineering –
-
Design and implement enterprise-grade data pipelines using Snowflake, including ingestion and transformation
- Must be strong in both Core and Semantic aspects
- Develop complex SQL transformations, stored procedures and Dynamic tables inside Snowflake to enable near real-time and batch processing
- Implement Snowflake data sharing, data marketplace integrations
- Engineer Snowpipe and Kafka-to-Snowflake streaming ingestion pipelines also handling high throughput event data at scale
- Optimize Snowflake cluster performance – virtual warehouse sizing, query profiling, clustering keys
- Architecture, design aspects, performance tuning, time travel, warehouse concepts – scaling, clustering, micro-partitioning
-
Experience with SnowSQL, Snowpipe
-
Data Integration aspects –
-
Design and maintain end-to-end ETL/ELT pipelines using Apache Airflow
- Experience in building reusable parameterized data ingestion pipelines/frameworks is beneficial
-
Thorough on data quality checks
-
AI and Data Science –
-
Integrate AI/LLMs with data pipelines via Python UDFs or API callouts – enabling text analytics, semantic search and GEN-AI augmented workflows
- Experience with Python based frameworks – scikit learn, PyTorch, TensorFlow
- Experience with NLP and text-mining techniques on unstructured data to identify actionable information
-
Time-series forecasting, anomaly detection and propensity modeling
-
Experience with Data Visualization aspects
-
Hands-on experience with writing Complex queries using – Joins, Self Joins, Views, Materialized Views, Cursor also Recursive, use of GROUP BY, PARTITION BY functions / SQL Performance tuning
-
Hands-on experience with ETL and Dimensional Data Modelling – Slowly Changing Dimensions (SCD – Type 1, 2, 3)
-
Good understanding of concepts like schema types, table types – fact-dimension etc. like how to design a dimension vs fact, design considerations factored etc.
-
Proficiency in Python scripting/programming – using Pandas, PyParsing, Airflow.
-
Pandas, Tableau server modules, Numpy, Datetime, Apache Airflow related modules, APIs
- Data Pipeline automation
-
Strong Python programming skills
-
Actively participating in discussions with business to understand requirements, perform thorough impact analysis and provide suitable solutions.
Key Words to search in Resume
Snowflake, Advanced SQL, Dimensional Data Modelling (Slowly Changing Dimensions), Python, AI, Data Science, Data Visualization
Location: Sunnyvale, CA
Salary range: $80,000-$140,000 a Year.
Location
Sunnyvale, CA
Job Function
TECHNOLOGY
Role
Engineer
Job Id
412666
Desired Skills
SQL | ETL Testing | SNOW
Salary Range
$80,000-$140,000 a year
Key Qualifications
MUST HAVE:
-
Snowflake Data Engineering –
-
Design and implement enterprise-grade data pipelines using Snowflake, including ingestion and transformation
- Must be strong in both Core and Semantic aspects
- Develop complex SQL transformations, stored procedures and Dynamic tables inside Snowflake to enable near real-time and batch processing
- Implement Snowflake data sharing, data marketplace integrations
- Engineer Snowpipe and Kafka-to-Snowflake streaming ingestion pipelines also handling high throughput event data at scale
- Optimize Snowflake cluster performance – virtual warehouse sizing, query profiling, clustering keys
- Architecture, design aspects, performance tuning, time travel, warehouse concepts – scaling, clustering, micro-partitioning
-
Experience with SnowSQL, Snowpipe
-
Data Integration aspects –
-
Design and maintain end-to-end ETL/ELT pipelines using Apache Airflow
- Experience in building reusable parameterized data ingestion pipelines/frameworks is beneficial
-
Thorough on data quality checks
-
AI and Data Science –
-
Integrate AI/LLMs with data pipelines via Python UDFs or API callouts – enabling text analytics, semantic search and GEN-AI augmented workflows
- Experience with Python based frameworks – scikit learn, PyTorch, TensorFlow
- Experience with NLP and text-mining techniques on unstructured data to identify actionable information
-
Time-series forecasting, anomaly detection and propensity modeling
-
Experience with Data Visualization aspects
-
Hands-on experience with writing Complex queries using – Joins, Self Joins, Views, Materialized Views, Cursor also Recursive, use of GROUP BY, PARTITION BY functions / SQL Performance tuning
-
Hands-on experience with ETL and Dimensional Data Modelling – Slowly Changing Dimensions (SCD – Type 1, 2, 3)
-
Good understanding of concepts like schema types, table types – fact-dimension etc. like how to design a dimension vs fact, design considerations factored etc.
-
Proficiency in Python scripting/programming – using Pandas, PyParsing, Airflow.
-
Pandas, Tableau server modules, Numpy, Datetime, Apache Airflow related modules, APIs
- Data Pipeline automation
-
Strong Python programming skills
-
Actively participating in discussions with business to understand requirements, perform thorough impact analysis and provide suitable solutions.
Key Words to search in Resume
Snowflake, Advanced SQL, Dimensional Data Modelling (Slowly Changing Dimensions), Python, AI, Data Science, Data Visualization
Location: Sunnyvale, CA
Salary range: $80,000-$140,000 a Year.
Location
Sunnyvale, CA
Job Function
TECHNOLOGY
Role
Engineer
Job Id
412666
Desired Skills
SQL | ETL Testing | SNOW
Salary Range
$80,000-$140,000 a year
See All 175+ Data Integration Engineer Jobs
Jump back to the full list of openings and apply to any data integration engineer role that fits.
Find JobsData Integration Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Amazon5
- Harvey5
- Meta4
- Netflix4
- Alrek Business Solutions3
Top Industries Hiring
- Technology & Software62
- Consulting & Professional Services19
- Electronics & Hardware10
- Healthcare & Medical Services9
- Education8
What Employers Look For
The qualifications that appear most often in data integration engineer jobs.
- Proficiency in SQL and at least one ETL or ELT tool such as Informatica, Talend, or dbt
- Experience designing and maintaining data pipelines on AWS, Azure, or Google Cloud Platform
- Strong programming skills in Python, Java, or Scala for pipeline scripting and automation
- Familiarity with data orchestration tools such as Apache Airflow, Prefect, or Luigi
- Experience integrating REST or SOAP APIs and working with JSON or XML data formats
- Bachelor's degree in computer science, information systems, or a related technical field
Tips for Your Data Integration Engineer Job Search
Tailor your resume for each pipeline tool
Hiring managers scan for specific tools, so list the ones you know, Apache Kafka, dbt, Talend, Informatica, or Fivetran, in a dedicated skills section. Bury them in job descriptions and automated screeners miss them entirely.
Quantify data volume and throughput on your resume
Vague phrases like 'built pipelines' don't tell a hiring manager much. Call out the scale: how many records per hour, the size of the datasets you moved, or the reduction in pipeline failure rates you achieved.
Filter openings by the cloud platform you know
Data integration roles vary sharply by cloud stack. AWS Glue, Azure Data Factory, and Google Cloud Dataflow require different skills. Target listings that match your strongest platform to avoid wasting time on mismatched technical screens.
Apply early to roles that fit
Migrate Mate lists data integration engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a working code sample before interviews
Many technical screens for data integration engineers include a live or take-home pipeline exercise. Have a clean, commented example using a common orchestration tool like Airflow or Prefect ready to walk through before your first interview.
Negotiate by comparing total compensation across offers
Base salary is only part of the picture for data integration roles. Remote flexibility, cloud certification reimbursement, and equity can shift the real value of two otherwise similar offers, so compare the full package before accepting or declining.
Data Integration Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data integration engineers?
The companies hiring the most data integration engineers right now include Amazon, Harvey, and Meta, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of June 2026. Healthcare systems, financial services firms, and enterprise software companies tend to post the highest volume of these roles consistently.
How many data integration engineer jobs are remote?
About 35% of data integration engineer openings are fully remote or hybrid as of June 2026, reflecting strong employer demand for distributed technical talent. Cloud-focused pipeline roles and API integration positions tend to have the highest remote availability, while on-premise data warehouse projects more often require some on-site presence.
How do you become a data integration engineer?
Start by building a foundation in SQL and Python, then learn at least one major ETL tool and a cloud data platform. Work through hands-on projects that involve pulling data from multiple source systems and loading it into a target warehouse or lake. A degree in computer science or information systems helps, but a portfolio of working pipelines and demonstrable tool experience carries significant weight with hiring teams.
Can you get hired as a data integration engineer without experience?
Entry-level data integration roles do exist, particularly at companies that need help modernizing legacy pipelines or handling high-volume batch processing. Build practical skills by completing a personal project that ingests data from a public API, transforms it, and loads it into a cloud database. Certifications from AWS, Google Cloud, or Microsoft Azure also signal readiness to hiring managers when direct work experience is limited.
What does the data integration engineer interview process look like?
The process typically starts with a recruiter screen focused on your tool experience and career background, followed by a technical phone interview covering SQL, data modeling, and pipeline design concepts. A take-home or live coding exercise often involves writing a transformation script or debugging a broken pipeline. Final rounds usually include a system design discussion where you walk through how you would architect an end-to-end data integration solution for a given business scenario.
Where can I find and apply to data integration engineer jobs?
You can find and apply to data integration engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings to find roles that match your tool experience and seniority level, then apply directly to each one that fits.
See All 175+ Data Integration Engineer Jobs
Jump back to the full list of openings and apply to any data integration engineer role that fits.
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