Staff Data Platform Engineer

CertifyOS

. Lead a team of engineers to deliver data platform initiatives end to end .

Posted 11/5/2026full-timeRemote • IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and leading data platforms, with a strong focus on data architecture, reliability, and performance. Proficient in Python, PySpark, and distributed systems, with a proven track record in entity resolution and MDM within regulated environments.

Highest-signal resume keywords

Expert PythonProduction PySparkEntity Resolution and MDMOLTP and OLAP ExpertiseStream Processing and Event-Driven Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureDistributed SystemsData ReliabilityPerformance BenchmarkingData Products OwnershipChange Data CaptureData ObservabilityAI/ML WorkloadsData MeshSchema Registries

Soft Skills

Mentoring EngineersClear Communication

Tools & Technologies

KafkaKinesisAWS GlueAirflowDbtAuroraPostgreSQLMySQLRedshiftSnowflake

Industry Keywords

HealthcareRegulated EnvironmentsPIIPHI

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSCassandraDistributed SystemsDynamoDBKafkaMongoDBMySQLNoSQLPostgresPySparkPythonSnowflake

About the role

Key responsibilities & impact

Lead a team of engineers to deliver data platform initiatives end to end
Own data architecture across OLTP, OLAP, and NoSQL stores
Design and operate distributed real-time and batch pipelines in Python and PySpark using Kafka, Kinesis, SNS/SQS
Lead entity resolution and MDM architecture for provider data
Build and own data products with clear contracts, SLAs, and consumers
Be accountable for data reliability, freshness, quality, and cost, with observability to prove it
Drive performance and scale work by profiling, benchmarking, and re-architecting as volume grows
Build data foundations for AI/ML workloads
Mentor engineers, raise the bar on design and code review, and partner with product, analytics, and ML
Join the on-call rotation and turn incidents into systemic fixes

Requirements

What you’ll need

10+ years building production data systems, including Staff or Tech Lead scope
Led a team of engineers to deliver complex data systems
Expert Python and production PySpark
Deep distributed systems knowledge: consistency, partitioning, idempotency, backpressure, failure handling
Significant entity resolution and MDM experience: matching, deduplication, survivorship, golden records at scale
Built and owned data products used by other teams
Shipped measurable performance and scale improvements
Expertise across OLTP (Aurora, PostgreSQL, MySQL), OLAP (Redshift, Athena, Snowflake, ClickHouse), and NoSQL (MongoDB, DynamoDB, Cassandra)
Stream processing and event-driven architecture (Kafka, Kinesis, SNS/SQS)
Data observability in practice: quality checks, lineage, monitoring, alerting
Built data platforms that serve ML workloads
Influences technical direction across teams and communicates clearly with non-technical stakeholders
Experience handling sensitive data (PII/PHI)
Fluent with AI-assisted development (Cursor, Claude Code, MCP)
Change Data Capture (CDC) patterns
Data mesh or data-platform-as-a-product approaches
Schema registries or data catalogs
Orchestration tools (AWS Glue, EMR, Managed Flink, Airflow/MWAA, dbt)
Healthcare or other regulated environments

Benefits

Comp & perks

100% coverage of health, dental, and vision insurance premiums for employees
Unlimited PTO for US-based team
At least two weeks off each year to recharge for US-based team
Health insurance for employees in India
Statutory leave benefits for employees in India
Additional wellness (menstrual) leave for women in India