Senior Data Engineer

Bidgely

. Build and run central data platform features on Databricks and Snowflake .

Posted 11/11/2026full-timeRemote • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable data platforms using Databricks and Snowflake, with a strong focus on data quality, governance, and pipeline optimization. Proficient in designing ELT/ETL workflows and implementing CI/CD practices for data engineering.

Highest-signal resume keywords

Databricks ProficiencySnowflake ProficiencyApache Spark with Python or ScalaProduction Experience with Apache AirflowData Governance and Quality Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringELT/ETL Pipeline DevelopmentSQL ProficiencyDelta Lake ManagementStreaming Pipeline DevelopmentAI Agent DevelopmentKimball Dimensional ModellingCI/CD Pipeline FamiliarityWorkflow OrchestrationData Quality Checks

Soft Skills

Strong CommunicationStakeholder ManagementIndependent Work

Tools & Technologies

DatabricksSnowflakeApache SparkKafkaApache AirflowLangGraphGitAWSAzureGCP

Industry Keywords

Data GovernanceData LineageRegulatory ComplianceRBAC Security PoliciesAgile Development

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudETLGoogle Cloud PlatformIoTKafkaPythonScalaSnowflakeSparkSQLTableau

About the role

Key responsibilities & impact

Build and run central data platform features on Databricks and Snowflake
Design, build, and maintain scalable batch and streaming ELT/ETL pipelines and large-scale datasets
Build AI agents and agentic data products using Databricks, Snowflake, LangGraph, and FastMCP
Implement automated data quality checks, end-to-end lineage tracking, and RBAC security policies
Monitor and optimize Databricks and Snowflake compute and storage costs
Translate business requirements into scalable, well-modelled analytical solutions with product managers, BI engineers, and data scientists
Support Kimball dimensional and 3NF data modelling for AWB reporting and analytics
Contribute to CI/CD pipelines for data workflows, including version control, automated testing, and deployment standards
Review code and share best practices
Translate architectural designs into production-ready, modular, reusable, loosely coupled, and testable code
Deliver core data platform features end to end in production within the first 12 months
Deliver agent-ready datasets and tools for LangGraph agents and MCP tools
Ensure data quality, lineage, and RBAC for every dataset built
Keep pipeline compute and storage costs within agreed budgets
Individual contributor role with no direct reports

Requirements

What you’ll need

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
Strong SQL proficiency on Databricks and Snowflake
Strong coding skills
Hands-on experience with Delta Lake, 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 workflows with Apache Airflow; mandatory
Hands-on experience building AI agents with LangGraph or LangChain and agent features of Databricks and Snowflake, such as Genie and Cortex
Experience using AI code-generation tools in day-to-day engineering work
Understanding of data governance, data lineage, quality monitoring, regulatory compliance, RBAC, and governance frameworks
Experience building production-grade data pipelines on AWS, Azure, or GCP with reliability and observability focus
Understanding of Kimball dimensional modelling, star/snowflake schemas, and 3NF normalization
Familiarity with CI/CD pipelines, Git-based workflows, and DevOps practices for data engineering
Strong communication and stakeholder management skills
Ability to work independently across time zones
Bonus: exposure to Databricks and Snowflake Native Applications, data clean rooms, or Marketplace integrations
Bonus: experience with dbt models, Prefect, dbt Cloud, semantic layer tools such as Looker or Tableau, Agile/product-based development, cost optimization, energy/utilities/IoT analytics, or MLOps

Benefits

Comp & perks

Remote work arrangement
No other benefits, perks, or compensation extras are specified