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Data Ingestion Jobs (NOW HIRING)

Visa Hunt

$140 - $210/hr

As a member of the Data Team, your mission is to build and operate the ingestion systems that turn the open web and other large-scale data sources into reliable, well-structured corpora for training ...

Aledade PBC

Staff Software Engineer- Data Ingestion

Expertise in designing, optimizing, and orchestrating robust data pipelines (ETL/ELT) and ingestion systems for large-scale, real-time, and batch processing. * Experience managing data warehouses (e ...

Roku

Sr. Solutions Engineer, Ad Data Ingestion

Boston, MA · On-site

$123K - $175K/yr

Roku is seeking a Senior Solutions Engineer, Ad Data Ingestion to join our Solutions Engineering team supporting Roku's advertising business. Roku's advertising business runs on the quality of the ...

Roku

Sr. Solutions Engineer, Ad Data Ingestion

Austin, TX · On-site

$123K - $175K/yr

Roku is seeking a Senior Solutions Engineer, Ad Data Ingestion to join our Solutions Engineering team supporting Roku's advertising business. Roku's advertising business runs on the quality of the ...

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Data Ingestion information

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How much do data ingestion jobs pay per year?

As of Sep 6, 2026, the average yearly pay for data ingestion in the United States is $126,123.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,000.00 and $159,000.00 per year, depending on experience, location, and employer.

What is data ingestion?

Data ingestion is the process of collecting and importing data from various sources into a storage or processing system, such as a database, data warehouse, or data lake. It involves transferring raw data, which may come from different formats and platforms, into a centralized location for analysis and further processing. Data ingestion can be done in real-time (streaming) or in batches, depending on business needs and data velocity. This process is crucial for organizations to make informed decisions based on comprehensive, up-to-date information.

What are the key skills and qualifications needed to thrive as a data ingestion specialist?

To thrive as a Data Ingestion Specialist, you need a strong understanding of data pipelines, ETL processes, and experience with databases, often supported by a degree in computer science or related fields. Familiarity with tools such as Apache Kafka, Apache NiFi, SQL, and cloud platforms like AWS or Azure is typically required, along with knowledge of data integration frameworks. Strong problem-solving skills, attention to detail, and effective communication set top performers apart in this role. These skills are crucial to ensure the accurate, efficient, and secure movement of data across systems, supporting reliable analytics and business decisions.

What are some common challenges faced in a data ingestion role, and how can they be addressed?

Professionals working in data ingestion often encounter challenges such as handling diverse data formats, ensuring data quality, and maintaining data pipeline reliability. Managing large volumes of data from multiple sources can lead to issues with consistency, latency, and error handling. To address these, it is essential to implement robust validation processes, use scalable data ingestion tools, and closely collaborate with data engineering and source system teams. Continuous monitoring and regular pipeline optimization are also key practices to ensure smooth data flow and integrity.

What is the difference between Data Ingestion vs Data Analyst?

AspectData IngestionData Analyst
Required CredentialsKnowledge of ETL tools, SQL, basic scriptingDegree in statistics, data science, or related field; SQL, Excel, visualization skills
Work EnvironmentData engineering teams, IT departments, cloud platformsBusiness units, analytics teams, reporting environments
Industry UsageData pipelines, data warehouses, big data platformsData interpretation, reporting, decision support

Data ingestion involves collecting and importing data into storage systems, focusing on data pipelines and infrastructure. Data analysts interpret and analyze this data to generate insights. While data ingestion prepares data for analysis, data analysts focus on understanding and communicating data findings. Both roles are essential in data-driven organizations but serve different functions within the data lifecycle.

What does data ingestion do?

Data ingestion involves collecting and importing data from various sources into a storage or processing system, such as data warehouses or data lakes. It is a key step in data pipelines, enabling data analysis, reporting, and machine learning applications. Data ingestion requires knowledge of tools like ETL processes, APIs, and scripting languages to ensure efficient and accurate data transfer.

What skills are needed for data ingestion?

Data ingestion roles require skills in data processing, scripting languages like Python or SQL, and familiarity with data integration tools such as Apache NiFi or Talend. Knowledge of database management, data formats, and basic understanding of cloud platforms like AWS or Azure are also important for efficient data pipeline development.
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Data Ingestion jobs near you
Infographic showing various Data Ingestion job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $126,123 per year, or $60.6 per hour.

Data ingestion Expert / Data Engineer

3B Staffing LLC

Manhattan, NY • On-site

$126K - $151K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Title -- Data ingestion Expert / Data Engineer

PYTHON - must be good in python

Good in SQL

Prior experience in a large variety of alternative data sets

Must have financial services

What is Alternative Data Sets -
NON- Market data - data from a vendor like consumer spending behavior or web traffic behavior, various kinds of data you get from web scrapes

Alternative data hedge funds use non-traditional data, like satellite imagery, social media sentiment, geolocation, and web scraping, to find investment signals missed by conventional research, offering early insights into market trends, consumer behavior, and company performance to gain an edge over rivals. This diverse data, often large and complex (big data), comes from outside a company and helps funds spot opportunities and risks before they appear in standard reports, with most hedge funds now integrating some form of it.

Common Sources of Alternative Data

  • Geolocation Data : Foot traffic, device movement to gauge retail/foot traffic.
  • Social Media & Web Data : Sentiment analysis from Twitter, Reddit, review sites, web scraping for trends.
  • Satellite Imagery : Tracking parking lots, oil storage, construction to predict retail/commodity activity.
  • Credit Card & Transaction Data : Aggregated purchase data to see consumer spending patterns.
  • Web Traffic & App Downloads : Real-time indicators of product adoption and company health.

How Hedge Funds Use It

  • Early Signals : Spotting shifts in demand or sentiment before earnings reports.
  • Competitive Edge : Gaining insights into consumer perception and competitor performance.
  • Quantitative Models : Feeding large datasets into complex algorithms for predictive power.

Key Providers & Tools

  • Data Vendors:

Companies like YipitData, Quandl (now part of Nasdaq), and others gather and process this data.

  • Platforms:

Services like AlphaSense and AlternativeSoft help manage and analyze this information.

Challenges

  • Data Quality: Ensuring data is accurate, relevant, and not misleading.
  • Volume & Complexity: Handling massive, unstructured datasets requires advanced tech.

Integrate with APIs and bring into Snowflake

Write ETL process, ingest from APIs - S3 Buckets - STFP servers - Snowflake vendor shares.

Required to write the code to put it into snowflake as a starting point - Data engineering work is to put it into snowflake

Next part of the role is to --

Integrate the raw data they've ingested into their analytics systems - Which is internally built Python tools and SQL processes they've developed. They have Internally built Python libraries. PREFECT is an orchestration tool. (Prefect.io)

DBT - a data transformation tool THIS IS THE TOOL KIT.

Candidate must be users of existing tools and processes and someone who has enough experience to suggest improvements to things , they have a non-perfect system and they want someone who can contribute ideas and do the necessary work to improve their data processes. They want someone who can build data quality and alerting tools and building leveraging DBT *** for work flows and adding features to existing things what they've already built. Help out in every way possible.

PYTHON SKILLS THEY MUST HAVE -
Numpy, SciPy, Pandas - they have a fairly typical data manipulation stack in python. But we're also open to other libraries that they don't use

Like - Polars, Dask, PySpark.

WORK FAST WITH EXPERIENCE IN THE FINANCIAL (may another hedge fund)

Ideally from the financial industry where they have worked with a variety of data sets in a time pressure environment. (what they are not going to get is 2 months to solve this problem. What you will get -- "here are the 10 data sets to process in the next 2 weeks, let's make this happen and streamline processes as new data comes in and you get more efficient.

On going que of data set ingestion projects

ALSO

Mixed in with the above is improvement projects, this new feature in data loading project to get more visibility to the project for the stakeholders

Python on Windows. We work in a Virtual machine based environment. They work instead a slightly less modern - environment. Virtual machine and this influences how they think of problem solving.

Normal hours are 9-6 hours-(someone builds a data process and it breaks you would have to have some amount of ownership. ) They may need to start at 6 in the morning to fix the problem in the system they built themselves. (own what you built) 5 days a week, 4 days on site but also must - get up at 6 in the am to fix something to fix it.

Excellent communications and talk to other engineers and talk to the data scientists to talk to the business. Understand the down stream intent of using the data

Apply