Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 55.
From 12,766 similar postings that came down. Coming down is not always a hire.
Senior Data Engineer
Bangalore, Karnataka, India
Actively hiring, first seen by us 1 hour ago- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
What you’ll work on
Full postingSee the full posting for the role’s responsibilities.
- People management
Individual contributor stated — people management source
Contribute to CI/CD pipeline development for data workflows, including version control, automated testing, and deployment standards. Review code and share best practices on Databricks, Snowflake and data quality with the team. This is an individual contributor role with no direct reports. Translate architectural designs into high-quality, production-ready code by applying software design principles, design patterns, SOLID principles, and clean code practices. Build modular, reusable, loosely coupled, and testable components that support long-term scalability and maintainability.
Read the full posting
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 11, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Bidgely is hiring
1 open job
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
- Scala
- SQL
- AWS
- Azure
- Databricks
- dbt
- Delta Lake
- GCP
- Kafka
- Looker
- Prefect
- Snowflake
- Spark
- Tableau
- Airflow
Where the posting names them
- Play a key hands-on role in building and running central data platform features on Databricks and Snowflake, supporting the architects and managers who own the design and delivery. - Design, build, and maintain scalable batch and streaming ELT/ETL data pipelines and large-scale datasets using Apache Spark with Python or Scala, Delta, Kafka and Airflow. - Build AI agents and agentic data products on Databricks (for example Genie), Snowflake (for example Cortex) and LangGraph, and expose governed data to agents through MCP tools built with FastMCP.
- 5+ years of data engineering experience building central data platforms, large-scale pipelines, and production datasets; strong, hands-on proficiency in both Databricks and Snowflake is strictly required. - Strong proficiency in Apache Spark using Python or Scala on Databricks, and in SQL on both Databricks and Snowflake, for core data processing workloads. Strong coding skills are required. This is a hands-on engineering role. - Hands-on experience with Delta Lake as the primary table format, including MERGE, schema evolution, OPTIMIZE, VACUUM and performance tuning.
- Solid understanding of data governance concepts including data lineage, quality monitoring, regulatory compliance, RBAC, and governance frameworks. - Experience building production-grade data pipelines on cloud platforms (AWS, Azure, or GCP) with a focus on reliability and observability. - Good understanding of data modelling concepts including Kimball dimensional modelling, star/snowflake schemas, and 3NF normalization.
ROLES & RESPONSIBILITIES - Play a key hands-on role in building and running central data platform features on Databricks and Snowflake, supporting the architects and managers who own the design and delivery. - Design, build, and maintain scalable batch and streaming ELT/ETL data pipelines and large-scale datasets using Apache Spark with Python or Scala, Delta, Kafka and Airflow.
- Design, build, and maintain scalable batch and streaming ELT/ETL data pipelines and large-scale datasets using Apache Spark with Python or Scala, Delta, Kafka and Airflow. - Build AI agents and agentic data products on Databricks (for example Genie), Snowflake (for example Cortex) and LangGraph, and expose governed data to agents through MCP tools built with FastMCP. - Implement automated data quality checks, end-to-end lineage tracking and RBAC security policies for the datasets you build, following the team’s data governance framework.
- Implement automated data quality checks, end-to-end lineage tracking and RBAC security policies for the datasets you build, following the team’s data governance framework. - Monitor and optimize Databricks and Snowflake compute and storage costs, and work within the team’s budget and spend-alert policies. - Work closely with product managers, BI engineers, and data scientists to translate business requirements into scalable, well-modelled analytical solutions.
- Contribute to CI/CD pipeline development for data workflows, including version control, automated testing, and deployment standards. - Review code and share best practices on Databricks, Snowflake and data quality with the team. This is an individual contributor role with no direct reports. - Translate architectural designs into high-quality, production-ready code by applying software design principles, design patterns, SOLID principles, and clean code practices. Build modular, reusable, loosely coupled, and testable components that support long-term scalability and maintainability.
WHAT YOU’LL DELIVER IN YOUR FIRST 12 MONTHS - Deliver core data platform features end to end in production on Databricks and Snowflake, working with the architects and managers who own them. - Deliver agent-ready datasets and tools that LangGraph agents and MCP tools use in production.
WHAT YOU’LL BRING - 5+ years of data engineering experience building central data platforms, large-scale pipelines, and production datasets; strong, hands-on proficiency in both Databricks and Snowflake is strictly required. - Strong proficiency in Apache Spark using Python or Scala on Databricks, and in SQL on both Databricks and Snowflake, for core data processing workloads. Strong coding skills are required. This is a hands-on engineering role.
- Production experience orchestrating batch and streaming workflows with Apache Airflow (mandatory), including retries, backfills, dependencies and alerting. - Hands-on experience building AI agents with LangGraph (or LangChain) and with the agent features of Databricks and Snowflake, such as Genie and Cortex. Uses AI code-generation tools in day-to-day engineering work. - Solid understanding of data governance concepts including data lineage, quality monitoring, regulatory compliance, RBAC, and governance frameworks.
BONUS SKILLS / NICE TO HAVE - Exposure to Databricks and Snowflake Native Applications, data clean rooms, or Marketplace integrations. - Experience implementing, testing and documenting dbt models for data reliability, lineage tracking and governance.
- Experience working in Agile/product-based development environments with sprint planning, backlog management, and iterative delivery. - Databricks and Snowflake cost optimization experience: compute usage analysis, query profiling, clustering and partitioning design, materialized views, search optimization, resource monitors, budgets and spend alerts. - Background in the energy, utilities, or IoT analytics domain is a strong differentiator.
- Background in the energy, utilities, or IoT analytics domain is a strong differentiator. - Exposure to MLOps concepts and production deployment workflows for data science models integrated with Databricks and Snowflake.
- Exposure to Databricks and Snowflake Native Applications, data clean rooms, or Marketplace integrations. - Experience implementing, testing and documenting dbt models for data reliability, lineage tracking and governance. - Experience with other orchestration tools such as Prefect or dbt Cloud.
- Experience implementing, testing and documenting dbt models for data reliability, lineage tracking and governance. - Experience with other orchestration tools such as Prefect or dbt Cloud. - Experience supporting BI/Analytics teams through semantic layer development (e.g., Looker, Tableau, or similar tools).
- Strong proficiency in Apache Spark using Python or Scala on Databricks, and in SQL on both Databricks and Snowflake, for core data processing workloads. Strong coding skills are required. This is a hands-on engineering role. - Hands-on experience with Delta Lake as the primary table format, including MERGE, schema evolution, OPTIMIZE, VACUUM and performance tuning. - Hands-on experience building streaming pipelines with Kafka and Spark Structured Streaming, including duplicates, late data, checkpoints and delivery guarantees.
- Hands-on experience with Delta Lake as the primary table format, including MERGE, schema evolution, OPTIMIZE, VACUUM and performance tuning. - Hands-on experience building streaming pipelines with Kafka and Spark Structured Streaming, including duplicates, late data, checkpoints and delivery guarantees. - Production experience orchestrating batch and streaming workflows with Apache Airflow (mandatory), including retries, backfills, dependencies and alerting.
- Experience with other orchestration tools such as Prefect or dbt Cloud. - Experience supporting BI/Analytics teams through semantic layer development (e.g., Looker, Tableau, or similar tools). - Experience working in Agile/product-based development environments with sprint planning, backlog management, and iterative delivery.
- Hands-on experience building streaming pipelines with Kafka and Spark Structured Streaming, including duplicates, late data, checkpoints and delivery guarantees. - Production experience orchestrating batch and streaming workflows with Apache Airflow (mandatory), including retries, backfills, dependencies and alerting. - Hands-on experience building AI agents with LangGraph (or LangChain) and with the agent features of Databricks and Snowflake, such as Genie and Cortex. Uses AI code-generation tools in day-to-day engineering work.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for Data Engineer roles
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.
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
ROLES & RESPONSIBILITIES
Play a key hands-on role in building and running central data platform features on Databricks and Snowflake, supporting the architects and managers who own the design and delivery.
Design, build, and maintain scalable batch and streaming ELT/ETL data pipelines and large-scale datasets using Apache Spark with Python or Scala, Delta, Kafka and Airflow.
Build AI agents and agentic data products on Databricks (for example Genie), Snowflake (for example Cortex) and LangGraph, and expose governed data to agents through MCP tools built with FastMCP.
Implement automated data quality checks, end-to-end lineage tracking and RBAC security policies for the datasets you build, following the team’s data governance framework.
Monitor and optimize Databricks and Snowflake compute and storage costs, and work within the team’s budget and spend-alert policies.
Work closely with product managers, BI engineers, and data scientists to translate business requirements into scalable, well-modelled analytical solutions.
Support data modelling initiatives using dimensional (Kimball) and normalized (3NF) techniques to serve AWB reporting and analytics needs.
Contribute to CI/CD pipeline development for data workflows, including version control, automated testing, and deployment standards.
Review code and share best practices on Databricks, Snowflake and data quality with the team. This is an individual contributor role with no direct reports.
Translate architectural designs into high-quality, production-ready code by applying software design principles, design patterns, SOLID principles, and clean code practices. Build modular, reusable, loosely coupled, and testable components that support long-term scalability and maintainability.
WHAT YOU’LL DELIVER IN YOUR FIRST 12 MONTHS
Deliver core data platform features end to end in production on Databricks and Snowflake, working with the architects and managers who own them.
Deliver agent-ready datasets and tools that LangGraph agents and MCP tools use in production.
Have data quality checks, lineage and RBAC in place for every dataset you build.
Bring the compute and storage cost of the pipelines you build within agreed budgets.
WHAT YOU’LL BRING
5+ years of data engineering experience building central data platforms, large-scale pipelines, and production datasets; strong, hands-on proficiency in both Databricks and Snowflake is strictly required.
Strong proficiency in Apache Spark using Python or Scala on Databricks, and in SQL on both Databricks and Snowflake, for core data processing workloads. Strong coding skills are required. This is a hands-on engineering role.
Hands-on experience with Delta Lake as the primary table format, including MERGE, schema evolution, OPTIMIZE, VACUUM and performance tuning.
Hands-on experience building streaming pipelines with Kafka and Spark Structured Streaming, including duplicates, late data, checkpoints and delivery guarantees.
Production experience orchestrating batch and streaming workflows with Apache Airflow (mandatory), including retries, backfills, dependencies and alerting.
Hands-on experience building AI agents with LangGraph (or LangChain) and with the agent features of Databricks and Snowflake, such as Genie and Cortex. Uses AI code-generation tools in day-to-day engineering work.
Solid understanding of data governance concepts including data lineage, quality monitoring, regulatory compliance, RBAC, and governance frameworks.
Experience building production-grade data pipelines on cloud platforms (AWS, Azure, or GCP) with a focus on reliability and observability.
Good understanding of data modelling concepts including Kimball dimensional modelling, star/snowflake schemas, and 3NF normalization.
Familiarity with CI/CD pipelines, Git-based workflows, and applying DevOps practices to data engineering projects.
Strong communication and stakeholder management skills with an ownership mindset and ability to work independently across time zones.
BONUS SKILLS / NICE TO HAVE
Exposure to Databricks and Snowflake Native Applications, data clean rooms, or Marketplace integrations.
Experience implementing, testing and documenting dbt models for data reliability, lineage tracking and governance.
Experience with other orchestration tools such as Prefect or dbt Cloud.
Experience supporting BI/Analytics teams through semantic layer development (e.g., Looker, Tableau, or similar tools).
Experience working in Agile/product-based development environments with sprint planning, backlog management, and iterative delivery.
Databricks and Snowflake cost optimization experience: compute usage analysis, query profiling, clustering and partitioning design, materialized views, search optimization, resource monitors, budgets and spend alerts.
Background in the energy, utilities, or IoT analytics domain is a strong differentiator.
Exposure to MLOps concepts and production deployment workflows for data science models integrated with Databricks and Snowflake.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore, Karnataka, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.