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Day 212
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Work setup in the employer’s posting: hybridFrom the employer’s posting
Employment type Full Time
Employment type in the employer’s posting: Full TimeFrom the employer’s posting
Design and implement analytical platforms that provide dashboards to customers
Develop and maintain data warehouse schemas, fact tables and dimensions
Oversee data propagation from source databases to warehouse tools
Keep the architecture extensible and scalable for future needs
Design and implement analytical platforms that provide insightful dashboards to customers.
Develop and maintain data warehouse schemas, such as star schemas, fact tables, and dimensions
Oversee data propagation processes from source databases to warehouse-specific databases/tools
Ensure the architectural design is extensible and scalable to adapt to future needs.
3+ years of Backend and Infrastructure Experience with a strong track record in development, architecture and design.
Proven experience in building high-scale data platforms.
Strong expertise in data warehouse schema design (star schema, fact tables, dimensions).
Hands-on experience with large-scale databases, high-scale messaging systems and real-time Job Queues.
B.E/B.Tech/M.E/M.Tech/PhD from tier 1 Engineering institutes with relevant work experience with a top technology company.
Requirement - - Qualification: B.E/B.Tech/M.E/M.Tech/PhD from tier 1 Engineering institutes with relevant work experience with a top technology company. - 3+ years of Backend and Infrastructure Experience with a strong track record in development, architecture and design.
Open 7 monthsWe first saw this posting 212 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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Day 212
Up longer than most. Similar jobs usually come down between day 6 and day 56.
From 13,630 similar postings that came down. Coming down is not always a hire.
3 open jobs
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SQL Proficiency: Advanced SQL skills for complex queries, indexing, and performance tuning. Programming Skills: Strong in Python or Java for building custom data pipelines and handling advanced data transformations. Data Integration: Experience with real-time data integration tools like Apache Kafka, Apache Spark, AWS Glue, Fivetran, and Stitch.
ETL/ELT Processes: Expertise in ETL/ELT tools (e.g., Apache NiFi, Apache Airflow, Informatica, Talend, dbt) to facilitate data movement from source systems to the data warehouse. SQL Proficiency: Advanced SQL skills for complex queries, indexing, and performance tuning. Programming Skills: Strong in Python or Java for building custom data pipelines and handling advanced data transformations.
Programming Skills: Strong in Python or Java for building custom data pipelines and handling advanced data transformations. Data Integration: Experience with real-time data integration tools like Apache Kafka, Apache Spark, AWS Glue, Fivetran, and Stitch. Data Pipeline Management: Familiar with workflow automation tools (e.g., Apache Airflow, Luigi) to orchestrate and monitor data pipelines.
Data Modelling: Skilled in designing data warehouse schemas (e.g., star and snowflake schemas), with experience in fact and dimension tables, as well as normalization and denormalization techniques. Data Warehousing & Storage Solutions: Proficient with platforms such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. ETL/ELT Processes: Expertise in ETL/ELT tools (e.g., Apache NiFi, Apache Airflow, Informatica, Talend, dbt) to facilitate data movement from source systems to the data warehouse.
Data Warehousing & Storage Solutions: Proficient with platforms such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. ETL/ELT Processes: Expertise in ETL/ELT tools (e.g., Apache NiFi, Apache Airflow, Informatica, Talend, dbt) to facilitate data movement from source systems to the data warehouse. SQL Proficiency: Advanced SQL skills for complex queries, indexing, and performance tuning.
Nice to have: - Experience with Google Cloud, Django, Postgres, Celery, Redis. - Some experience with AI Infrastructure and Operations.
Data Integration: Experience with real-time data integration tools like Apache Kafka, Apache Spark, AWS Glue, Fivetran, and Stitch. Data Pipeline Management: Familiar with workflow automation tools (e.g., Apache Airflow, Luigi) to orchestrate and monitor data pipelines. APIs and Data Feeds: Knowledgeable in API-based integrations, especially for aggregating data from distributed sources.
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New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Full Time
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