Top Data Engineer Jobs in Bengaluru

Lead the architecture and hands-on development of CheQ’s data platform. Define data architecture, canonical business models, governance, quality, lineage, self-service analytics, AI/ML readiness, and cost controls. Build critical platform components, establish engineering standards and operating models, and hire and lead a small data engineering team. The role requires designing scalable batch, streaming, and transactional systems for a regulated fintech environment while enabling trusted analytics and future machine-learning workloads.

Designs, builds, and maintains scalable ETL pipelines connecting Salesforce with Snowflake and other data systems. The role manages Salesforce APIs and bulk data loading, writes and optimizes SQL and Python for data transformation, conducts Git-based code reviews, and monitors pipeline performance, availability, and data integrity.

Designs and maintains enterprise Azure data solutions using Synapse Analytics and Databricks. Develops scalable pipelines, data models, SQL optimizations, and PySpark/Python transformations. Creates Power BI dashboards, implements data governance with Unity Catalog, and explores Generative AI through Databricks Genie. Collaborates with cross-functional teams to define requirements and deliver high-quality analytics engineering solutions.

Build and optimize high-throughput market-data pipelines, database schemas, scheduled workflows, REST APIs, and data-quality systems using Python, R, and PostgreSQL. Monitor pipeline health, analyze execution data, support transaction-cost research, and collaborate with quants and product managers to productionize analytics for global trading systems and clients.

Build and maintain observability across enterprise data pipelines by implementing data quality, freshness, volume, and schema monitoring. Configure tooling such as Monte Carlo, Great Expectations, CloudWatch, and Datadog; create alerts, maintain SLAs and runbooks, and perform incident triage and root cause analysis. The role requires AWS data services, Python, SQL, and data quality framework expertise.

Design and build AWS-based ingestion pipelines for structured and unstructured data supporting analytics and AI/GenAI use cases. Prepare AI-ready datasets through document parsing, chunking, and embedding workflows, while developing reusable, metadata-driven ingestion frameworks.

Build and support AWS-based ETL pipelines for PRA regulatory and reporting data within the NURO program. Translate business reporting requirements into data models, maintain data lineage, ensure auditability and accuracy, and follow data governance and GxP-aware practices.

Build and maintain scalable consumer-facing data pipelines on Databricks using PySpark, Spark SQL, and Delta Lake. Implement medallion architecture, Unity Catalog, and Workflows. Optimize cluster performance and costs, and monitor and troubleshoot production jobs. The role requires data engineering experience with Databricks, PySpark, SQL, Git, and CI/CD; pharma or consumer data experience and Databricks certification are preferred.

Develops and operates a data platform supporting affiliate markets. Builds and maintains ingestion and transformation pipelines, harmonizes multi-country data into common models, and provides production support, monitoring, and break-fix assistance through a shift rotation.

Designs and maintains scalable Azure data infrastructure, including ADLS and Delta Lake architectures, ETL pipelines with Azure Data Factory and PySpark, and data warehouses using Azure Synapse Analytics. Develops Python and SQL scripts for automation, data quality, and analytics; integrates diverse data sources and collaborates with cross-functional teams to deliver reliable data solutions.

Designs, builds, and maintains scalable Azure data platforms and end-to-end pipelines. Develops streaming solutions with Airflow, Kafka, and Azure Functions; manages Cosmos DB and Blob Storage; implements governance and lineage through Azure Purview; collaborates on technical specifications; and monitors and optimizes pipeline performance.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and scale end-to-end ETL/ELT pipelines (batch and near-real-time), design ingestion systems, optimize cloud data warehouses, enforce data quality and monitoring, define data models/contracts, enable ML/AI workflows, and operate data infrastructure using Python, PostgreSQL, MongoDB, and AWS.

Own the architecture and evolution of Albert OS’s data platform, including lakehouse infrastructure, analytical pipelines, transactional systems, data contracts, and modeling standards. Build reliable batch and streaming pipelines, optimize SQL and NoSQL performance, enforce security and tenant isolation, and enable AI/ML workloads. Establish observability practices, mentor engineers, and lead cross-functional architectural decisions.

Lead modernization of production data pipelines in Stockholm, designing architecture and migrating systems while preserving existing outputs. Build hands-on solutions with Airflow, Spark, dbt, BigQuery, and Python; establish testing, code review, incident management, and release processes; and mentor a data engineer. The role also addresses encryption, data retention, deletion compliance, reliability, and long-term pipeline ownership.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Design, build, test, maintain, and monitor scalable Lakeflow Spark Declarative Pipelines using Event Hubs and medallion architecture. Gather requirements, transform data across pipeline layers, troubleshoot complex issues, and create technical documentation. The role requires proficiency in Python, SQL, PySpark, Spark SQL, Databricks, Azure, and Unity Catalog, with streaming and declarative pipeline experience preferred.

Leads data architecture and engineering for the META initiative, owning solution design, data modeling, pipeline architecture standards, code reviews, engineer mentorship, performance tuning, and technical roadmap decisions. Partners with architects and business leaders to guide implementation and ensure scalable, high-performing data solutions.

Owns ingestion, data modeling, reconciliation, validation, and lineage for a financial billing platform on GCP. Designs auditable, idempotent BigQuery pipelines; implements data quality controls, quarantine processes, effective-dated reference data, and accurate monetary handling. Develops tested Python and advanced SQL, collaborates with engineering and finance stakeholders, and mentors team members. Preferred experience includes invoicing, regulated or audited finance environments, and additional GCP services.

Run and monitor data jobs on Linux servers, recover failures, configure project workflows, and write PostgreSQL and ClickHouse queries. Develop Python, SQL, Bash, or TypeScript scripts for quality, completion, pay, and delivery checks. Package datasets in cloud storage, track client deliverables, interpret a TypeScript codebase, communicate findings to operations and clients, and identify recurring manual work for platform engineering.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Build and optimize large-scale healthcare data pipelines supporting AI applications. Manage EHR and clinical data using FHIR, HL7, Epic Clarity, Airflow, databases, data lakes, and vector databases. Develop AI-enabled retrieval and orchestration workflows involving RAG, GraphRAG, LLMs, and tool calling. Collaborate with data science and backend teams, validate generative AI systems, maintain secure data exchanges, and improve scalability, accuracy, latency, and performance.

Architects and manages scalable data ingestion pipelines connecting SaaS, cloud, on-premises, non-SAP, and Oracle data sources. Administers Fivetran, establishes secure connectivity, implements CDC and incremental replication, monitors pipeline performance, troubleshoots issues, and optimizes data transfer reliability and synchronization.

Build and scale backend APIs, distributed data pipelines, and data platform infrastructure. The role involves lakehouse and warehouse technologies, workflow orchestration, big data frameworks, infrastructure as code, and reliability monitoring. The engineer will design scalable, reliable, cost-efficient systems, collaborate with customers on integrations, and establish engineering best practices for enterprise AI data infrastructure.