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Staff Engineer, Data Platform
Bangalore, India
Actively hiring, first seen by us today- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: SALARIED_FT
From the employer’s posting
What you’ll work on
Full postingOwn architecture and evolution of Lakehouse, analytical and transactional data platforms
Design batch and streaming ingestion and transformation pipelines with strong data quality
Define data modeling standards (dimensional, medallion, schema-first)
Optimize SQL and NoSQL performance: query efficiency, partitioning, throughput
Enforce data security: encryption boundaries, access controls, tenant isolation
From the employer’s posting
Own the architecture and evolution of our core data platforms, spanning Lakehouse infrastructure, analytical pipelines, and transactional systems.
Design and build scalable data ingestion and transformation pipelines (batch and streaming) with a strong emphasis on data quality, reliability, and downstream usability.
Define data modeling standards across our stack, including dimensional, medallion, and schema-first patterns.
Drive performance optimization across SQL and NoSQL systems, with a focus on query efficiency, partitioning strategies, and throughput at scale.
Establish and enforce data security practices, including encryption boundaries, access controls, and tenant isolation.
What you’ll bring
All qualificationsCore experience
- 8+ years of experience in data engineering or a closely related discipline, with a focus on large-scale distributed systems.
- Hands-on experience with Databricks and lakehouse architecture patterns.
- Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models.
- Strong understanding of transactional systems design, including consistency models, write patterns, and operational reliability.
- Expertise in data modeling across both SQL and NoSQL ecosystems.
- Hands-on experience with AWS services including S3, Lambda, and IAM.
Preferred experience
- Knowledge of DynamoDB and PostgreSQL is a plus.
Qualification wording
8+ years of experience in data engineering or a closely related discipline, with a focus on large-scale distributed systems.
Hands-on experience with Databricks and lakehouse architecture patterns.
Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models.
Strong understanding of transactional systems design, including consistency models, write patterns, and operational reliability.
Expertise in data modeling across both SQL and NoSQL ecosystems.
Hands-on experience with AWS services including S3, Lambda, and IAM.
Knowledge of DynamoDB and PostgreSQL is a plus.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 7, 2026today
- Recorded sightings
- 1
- Employer says posted
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Albert Invent is hiring
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
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.
- Python
- SQL
- AWS
- Databricks
- DynamoDB
- Grafana
- NoSQL
- PostgreSQL
- S3
Where the posting names them
- Expertise in data modeling across both SQL and NoSQL ecosystems. - Strong programming skills in Python and Spark with an emphasis on clean, maintainable code. - Hands-on experience with AWS services including S3, Lambda, and IAM.
- Define data modeling standards across our stack, including dimensional, medallion, and schema-first patterns. - Drive performance optimization across SQL and NoSQL systems, with a focus on query efficiency, partitioning strategies, and throughput at scale. - Collaborate closely with API and Data Science teams to build robust pipelines and shared data contracts.
- Strong understanding of transactional systems design, including consistency models, write patterns, and operational reliability. - Expertise in data modeling across both SQL and NoSQL ecosystems. - Strong programming skills in Python and Spark with an emphasis on clean, maintainable code.
- Strong programming skills in Python and Spark with an emphasis on clean, maintainable code. - Hands-on experience with AWS services including S3, Lambda, and IAM. - Familiarity with observability stacks (Grafana, CloudWatch) and experience debugging production bottlenecks.
- 8+ years of experience in data engineering or a closely related discipline, with a focus on large-scale distributed systems. - Hands-on experience with Databricks and lakehouse architecture patterns. - Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models.
- Familiarity with observability stacks (Grafana, CloudWatch) and experience debugging production bottlenecks. - Knowledge of DynamoDB and PostgreSQL is a plus. - Excellent communication skills and a track record of influencing cross-functional technical decisions.
- Establish and enforce data security practices, including encryption boundaries, access controls, and tenant isolation. - Set standards for observability across data systems using tools like Grafana, CloudWatch, and OpenTelemetry. - Mentor engineers across the org and lead architectural decisions through review and documentation.
- Hands-on experience with AWS services including S3, Lambda, and IAM. - Familiarity with observability stacks (Grafana, CloudWatch) and experience debugging production bottlenecks. - Knowledge of DynamoDB and PostgreSQL is a plus.
Your next step
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Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 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.
About the role
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.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
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.
Job description
Albert's mission is to help chemists change the world. We build Albert OS, the first AI-native operating system for materials science, helping enterprise R&D teams capture their scientific knowledge, run smarter experiments, and bring better materials to market faster. Our data systems are at the heart of that mission
We're looking for a Staff Software Engineer, Data to lead the design and evolution of the data infrastructure that powers Albert OS. This is a high-impact role for someone who thinks deeply about data architecture across analytical and transactional systems, cares about correctness and reliability at scale, and can influence engineering direction across teams.
Responsibilities
- Own the architecture and evolution of our core data platforms, spanning Lakehouse infrastructure, analytical pipelines, and transactional systems.
- Design and build scalable data ingestion and transformation pipelines (batch and streaming) with a strong emphasis on data quality, reliability, and downstream usability.
- Define data modeling standards across our stack, including dimensional, medallion, and schema-first patterns.
- Drive performance optimization across SQL and NoSQL systems, with a focus on query efficiency, partitioning strategies, and throughput at scale.
- Collaborate closely with API and Data Science teams to build robust pipelines and shared data contracts.
- Establish and enforce data security practices, including encryption boundaries, access controls, and tenant isolation.
- Set standards for observability across data systems using tools like Grafana, CloudWatch, and OpenTelemetry.
- Mentor engineers across the org and lead architectural decisions through review and documentation.
Requirements
- 8+ years of experience in data engineering or a closely related discipline, with a focus on large-scale distributed systems.
- Hands-on experience with Databricks and lakehouse architecture patterns.
- Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models.
- Strong understanding of transactional systems design, including consistency models, write patterns, and operational reliability.
- Expertise in data modeling across both SQL and NoSQL ecosystems.
- Strong programming skills in Python and Spark with an emphasis on clean, maintainable code.
- Hands-on experience with AWS services including S3, Lambda, and IAM.
- Familiarity with observability stacks (Grafana, CloudWatch) and experience debugging production bottlenecks.
- Knowledge of DynamoDB and PostgreSQL is a plus.
- Excellent communication skills and a track record of influencing cross-functional technical decisions.
Key Competencies
- Builder Mindset: Wants to create foundational systems that others build on.
- Curiosity: Genuine interest in how your work enables scientific discovery.
- Rigor: Commitment to quality. AI makes mistakes confidently, and our customers won't accept hand-waving.
- Collaboration: Partners effectively with engineers across Product Engineering, AI/ML, and Enterprise Engineering.
- Adaptability: Comfortable in a dynamic, fast-moving environment where priorities shift.
Why Albert
- Revenue from day one. The product is loved and used by thousands of users.
- Small team. Flexible hours. Collaboration over process.
- No micro-management. You'll own your work.
- Building software for a sustainable future.
Source details
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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