Employment type in the employer’s posting: FULL_TIMEFrom the employer’s posting
We are looking for a self-sufficient BI Analyst / Data Engineer to join a small, high-impact analytics team.
The ideal candidate is comfortable operating independently across the data stack, takes ownership of their work, and communicates proactively with team members across time zones.
Design and maintain AWS Glue (PySpark) ETL jobs into a raw-core-presentation data lake
Orchestrate pipelines with AWS Step Functions, including retries and concurrency
Build and maintain Amazon QuickSight dashboards and Athena-backed datasets
Troubleshoot production pipeline failures and performance bottlenecks
Manage Apache Iceberg tables on S3, including MERGE, compaction and cleanup
We are looking for a self-sufficient BI Analyst / Data Engineer to join a small, high-impact analytics team. In this role you will own the full lifecycle of business intelligence delivery—from building and maintaining ETL pipelines in AWS to designing interactive dashboards in Amazon QuickSight. You will work closely with onshore stakeholders to translate business requirements into reliable, well-documented data products that drive operational and strategic decision-making.
The ideal candidate is comfortable operating independently across the data stack, takes ownership of their work, and communicates proactively with team members across time zones.
Design, build, and maintain AWS Glue (PySpark) ETL jobs that ingest data from multiple source systems
Develop and optimize AWS Step Functions workflows for pipeline orchestration, including parallel execution, error handling, retry logic, and concurrency management.
Build, publish, and maintain Amazon QuickSight dashboards and analyses that serve operational and executive audiences.
Troubleshoot production pipeline failures, diagnose performance bottlenecks (OOM errors, API timeouts, DPU tuning)
Work with Apache Iceberg table format on S3 for ACID-compliant analytics tables, including MERGE operations, compaction, snapshot expiration, and orphan file cleanup.
5–10+ years of professional experience in data engineering, BI development, or analytics engineering.
Proficiency in Python and SQL; experience writing PySpark transformations and Athena queries.
Experience building and maintaining ETL pipelines that ingest data from REST APIs or SaaS platforms (e.g., Salesforce, JIRA, Zendesk).
Familiarity with columnar/lakehouse table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.
Solid understanding of data warehouse concepts: dimensional modeling, slowly changing dimensions, incremental vs. full loads.
Experience with infrastructure-as-code tools such as AWS CDK, CloudFormation, or Terraform.
Familiarity with CI/CD pipelines (GitHub Actions, CodePipeline) for data engineering workflows.
Prior experience translating Tableau workbooks or Excel-based reports into another BI platform.
Knowledge of EventBridge-driven architectures and SQS/SNS messaging patterns.
Open 4 monthsWe first saw this posting 141 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 141
Up longer than most. Similar jobs usually come down between day 6 and day 56.
Well past HealthEdge’s usual: its jobs come down by about day 26.
From 13,630 similar postings that came down, and 16 of HealthEdge’s. Coming down is not always a hire.
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Strong hands-on experience with AWS Glue (PySpark), Step Functions, S3, Athena, and IAM. - Proficiency in Python and SQL; experience writing PySpark transformations and Athena queries. - Experience building and maintaining ETL pipelines that ingest data from REST APIs or SaaS platforms (e.g., Salesforce, JIRA, Zendesk).
Position Summary We are looking for a self-sufficient BI Analyst / Data Engineer to join a small, high-impact analytics team. In this role you will own the full lifecycle of business intelligence delivery—from building and maintaining ETL pipelines in AWS to designing interactive dashboards in Amazon QuickSight. You will work closely with onshore stakeholders to translate business requirements into reliable, well-documented data products that drive operational and strategic decision-making. The ideal candidate is comfortable operating independently across the data stack, takes ownership of their work, and communicates proactively with team members across time zones.
ETL / Data Pipeline Development - Design, build, and maintain AWS Glue (PySpark) ETL jobs that ingest data from multiple source systems such as Salesforce, JIRA, Azure DevOps, and Zendesk into a layered data lake architecture (raw → core → presentation). - Develop and optimize AWS Step Functions workflows for pipeline orchestration, including parallel execution, error handling, retry logic, and concurrency management.
- Design, build, and maintain AWS Glue (PySpark) ETL jobs that ingest data from multiple source systems such as Salesforce, JIRA, Azure DevOps, and Zendesk into a layered data lake architecture (raw → core → presentation). - Develop and optimize AWS Step Functions workflows for pipeline orchestration, including parallel execution, error handling, retry logic, and concurrency management. - Implement incremental loading strategies, schema evolution handling, and data quality checks within Glue scripts.
- Work with Apache Iceberg table format on S3 for ACID-compliant analytics tables, including MERGE operations, compaction, snapshot expiration, and orphan file cleanup. - Integrate supporting AWS services such as EventBridge, SQS, SNS, Athena, and CloudWatch into pipeline architectures. - Troubleshoot production pipeline failures, diagnose performance bottlenecks (OOM errors, API timeouts, DPU tuning), and implement fixes with minimal guidance.
- 5–10+ years of professional experience in data engineering, BI development, or analytics engineering. - Strong hands-on experience with AWS Glue (PySpark), Step Functions, S3, Athena, and IAM. - Proficiency in Python and SQL; experience writing PySpark transformations and Athena queries.
Preferred Qualifications - Experience with infrastructure-as-code tools such as AWS CDK, CloudFormation, or Terraform. - Familiarity with CI/CD pipelines (GitHub Actions, CodePipeline) for data engineering workflows.
- Knowledge of EventBridge-driven architectures and SQS/SNS messaging patterns. - AWS certifications (e.g., Data Analytics Specialty, Solutions Architect Associate) are a plus. What We Offer
- Working knowledge of Amazon QuickSight, including dataset creation, calculated fields, parameters, and dashboard publishing. - Familiarity with columnar/lakehouse table formats such as Apache Iceberg, Delta Lake, or Apache Hudi. - Solid understanding of data warehouse concepts: dimensional modeling, slowly changing dimensions, incremental vs. full loads.
- Create well-modeled QuickSight datasets backed by Athena queries over Iceberg tables, applying appropriate joins, calculated fields, and filters. - Translate business requirements and existing Tableau/Excel reports into QuickSight visuals, ensuring accuracy and usability. - Optimize dashboard performance, evaluating SPICE vs. Direct Query modes and managing refresh schedules.
- Exposure to PII detection/redaction practices or data governance frameworks. - Prior experience translating Tableau workbooks or Excel-based reports into another BI platform. - Knowledge of EventBridge-driven architectures and SQS/SNS messaging patterns.
- Implement incremental loading strategies, schema evolution handling, and data quality checks within Glue scripts. - Work with Apache Iceberg table format on S3 for ACID-compliant analytics tables, including MERGE operations, compaction, snapshot expiration, and orphan file cleanup. - Integrate supporting AWS services such as EventBridge, SQS, SNS, Athena, and CloudWatch into pipeline architectures.
- Build, publish, and maintain Amazon QuickSight dashboards and analyses that serve operational and executive audiences. - Create well-modeled QuickSight datasets backed by Athena queries over Iceberg tables, applying appropriate joins, calculated fields, and filters. - Translate business requirements and existing Tableau/Excel reports into QuickSight visuals, ensuring accuracy and usability.
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Yes. We’ve followed one other Senior Data Engineer job there since August 2026. It is still up.
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.
Sr Data Engineer
Position Summary
We are looking for a self-sufficient BI Analyst / Data Engineer to join a small, high-impact analytics team. In this role you will own the full lifecycle of business intelligence delivery—from building and maintaining ETL pipelines in AWS to designing interactive dashboards in Amazon QuickSight. You will work closely with onshore stakeholders to translate business requirements into reliable, well-documented data products that drive operational and strategic decision-making.
The ideal candidate is comfortable operating independently across the data stack, takes ownership of their work, and communicates proactively with team members across time zones.
Core Responsibilities
ETL / Data Pipeline Development
Dashboard & Reporting Development
General Team Responsibilities
Required Qualifications
Preferred Qualifications
What We Offer
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Bangalore
Working pattern and location restrictions need checking in the full posting.
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