We’re Hiring: ETL Developers – Multiple Levels (Remote Opportunities)

TechLoons

1 month ago

We’re expanding our team and looking for talented ETL Developers across various experience levels. With 26 open positions, this is a fantastic opportunity for professionals who are passionate about data engineering, ETL pipelines, and cloud-based data solutions. Whether you’re just starting your career or have years of experience, we have a role for you!

Position Details
Location: Fully Remote
Work Mode: Remote (Work from anywhere)

We’re hiring for the following levels:

🔹 Junior ETL Developer (1–3 Years Experience) – 18 Openings
🔹 Mid-Level ETL Developer (3–6 Years Experience) – 6 Openings
🔹 Senior ETL Developer (6–8 Years Experience) – 2 Openings

Category	Details
Job Roles	ETL Developer
Experience Required	1 to 8 Years
Education	Any Graduate
Job Locations	Remote
Job Type	Fulltime
Application Mode	Online
Salary Range	Based on Experience/Last Salary

What Does an ETL Developer Do?

An ETL (Extract, Transform, Load) Developer is responsible for designing, building, and maintaining data pipelines that move and transform raw data into usable formats for analytics and business intelligence.

Key responsibilities include:

Extracting data from multiple sources (databases, APIs, flat files).
Transforming data by cleaning, filtering, and structuring it for analysis.
Loading processed data into data warehouses or data lakes.
Optimizing ETL workflows for performance and scalability.
Troubleshooting data pipeline issues and ensuring data integrity.

Skills & Qualifications We’re Looking For

The ideal candidate will have experience in:

SQL & Database Management – Strong querying skills and familiarity with relational databases (MySQL, PostgreSQL, SQL Server).
ETL Tools – Hands-on experience with tools like Informatica, Talend, SSIS, Apache NiFi, or AWS Glue.
Cloud Data Platforms – Knowledge of AWS, Azure, or Google Cloud data services (Redshift, BigQuery, Snowflake).
Scripting & Automation – Proficiency in Python, Bash, or PowerShell for automating ETL tasks.
Data Warehousing Concepts – Understanding of star schema, snowflake schema, and dimensional modeling.
Big Data Technologies (For Senior Roles) – Experience with Spark, Hadoop, or Kafka is a plus.

Why Join Us?

100% Remote Work – Enjoy the flexibility of working from anywhere.
Career Growth – Work with cutting-edge technologies and upskill in data engineering.
Impactful Projects – Contribute to high-impact data solutions that drive business decisions.
Collaborative Culture – Be part of a team that values innovation and problem-solving.

How to Apply
If you’re excited about building robust ETL pipelines and working with large-scale data, we’d love to hear from you!

📩 Send your resume to: recruiterservices@oneteaminfotech.com

Let’s shape the future of data together—apply today!

recruiterservices@oneteaminfotech.com

FAQ’s (Frequently Asked Questions)

What is ETL and why is it important in data warehousing?

ETL stands for Extract, Transform, Load. It is a process used to extract data from different sources, transform it into a suitable format, and load it into a data warehouse. ETL is crucial because it ensures data is accurate, consistent, and ready for analysis.

Explain the difference between OLTP and OLAP systems.

OLTP (Online Transaction Processing) systems handle day-to-day transaction data and are optimized for fast insert, update, and delete operations. OLAP (Online Analytical Processing) systems are designed for analysis and reporting, optimized for complex queries and aggregations.

What are some common ETL tools you have worked with?

Common ETL tools include Informatica PowerCenter, Talend, Microsoft SSIS, Apache NiFi, Pentaho, and AWS Glue. (Customize based on your experience.)

How do you handle data quality issues during ETL?

I use data profiling to identify issues, apply cleansing techniques (such as removing duplicates, standardizing formats), and implement validation rules in the transformation stage to ensure data quality before loading.

What is FAQ?

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What is a slowly changing dimension (SCD)? Name its types.

A slowly changing dimension is a dimension that changes slowly over time, rather than frequently. The main types are:
Type 1: Overwrite old data.
Type 2: Add a new record with a version or timestamp.
Type 3: Add a new column to store previous values.

Write a SQL query to find duplicate records in a table.

SELECT column1, column2, COUNT() FROM table_name GROUP BY column1, column2 HAVING COUNT() > 1;

How do you optimize the performance of an ETL process?

By optimizing SQL queries, using indexing, partitioning large tables, parallel processing, minimizing data movement, and using incremental loads instead of full loads.

What is data partitioning and why is it useful?

Data partitioning divides a large table into smaller, more manageable pieces, improving query performance and ETL load times by allowing parallel processing and reducing I/O.

What is the difference between INNER JOIN and LEFT JOIN in SQL?

INNER JOIN returns only matching rows from both tables. LEFT JOIN returns all rows from the left table and matching rows from the right table; if there’s no match, NULLs are returned for right table columns.

How do you ensure data consistency between source and target systems?

By implementing checksums, row counts, and reconciliation reports to compare source and target data after ETL loads, and by using transactional control to ensure atomicity.

How do you ensure data consistency between source and target systems?

By implementing checksums, row counts, and reconciliation reports to compare source and target data after ETL loads, and by using transactional control to ensure atomicity.

What is data profiling and why is it important?

Data profiling analyzes source data to understand its structure, quality, and content. It helps identify data quality issues and informs the design of ETL processes.

Explain the concept of incremental loading.

Incremental loading involves loading only new or changed data since the last ETL run, rather than reloading the entire dataset. This improves efficiency and reduces processing time.

How do you handle schema changes in source systems?

By monitoring for schema changes, updating ETL mappings and transformations accordingly, and performing impact analysis before deploying changes to production.

What are some best practices for error handling in ETL processes?

1. Logging errors and exceptions
2. Sending alerts/notifications for failures
3. Implementing retry mechanisms
4. Isolating bad records for review
5. Maintaining audit trails for troubleshooting

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