Data Engineer

Coretek

. Design, build, and maintain idempotent, observable, and recoverable batch and streaming data pipelines .

Posted 9/20/2026full-timeKondapur • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining production data pipelines, with strong capabilities in Python development, data modeling, and performance tuning. Proficient in implementing data quality checks and collaborating with cross-functional teams to ensure data integrity and compliance.

Highest-signal resume keywords

Python DevelopmentData Pipeline EngineeringSQL Performance TuningAzure Data FactoryData Modeling Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignPySpark DataFrameSQL APIsData Quality ChecksVersion ControlInfrastructure as CodeStreaming Data ProcessingData ModelingRoot-Cause AnalysisCI/CD Practices

Soft Skills

Excellent CommunicationAnalytical Problem-SolvingConsultative Technical SkillsProject Management

Tools & Technologies

Azure Data FactoryDatabricksSynapse/FabricADLSTerraformDockerKubernetesKafkaEvent HubsDelta Lake

Industry Keywords

HIPAASOC 2PCIGDPR

Tech Stack

Tools & technologies

AzureDockerKafkaKubernetesPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Design, build, and maintain idempotent, observable, and recoverable batch and streaming data pipelines
Model data for analytics, including dimensional models, semantic layers, and curated marts
Integrate data from operational databases, SaaS APIs, files, and event streams while handling schema drift and late-arriving data
Build pipeline-integrated data quality checks for freshness, volume, uniqueness, and referential integrity
Define failure alerting and escalation processes
Own production pipelines through monitoring, on-call data incident response, root-cause analysis, and backfills
Tune performance and cost through partitioning, clustering, file sizing, and warehouse or cluster sizing
Apply version control, code review, CI/CD, automated testing, and infrastructure as code
Implement access controls, PII handling, retention, lineage, and audit requirements with security and compliance teams
Partner with analysts, data scientists, and product engineers to create durable data contracts
Maintain data dictionaries, lineage, and pipeline runbooks

Requirements

What you’ll need

5+ years building production data pipelines
Strong hands-on Python development for data engineering, including testing, packaging, and code review practice
Working knowledge of PySpark DataFrame and SQL APIs, joins and aggregations at scale, partitioning and shuffle behavior, and Spark UI diagnosis
Strong SQL skills, including window functions, query plans, and performance tuning
Hands-on experience with Azure Data Factory, Databricks, Synapse/Fabric, and ADLS
Solid data modeling fundamentals, including normalization, star schemas, and slowly changing dimensions
Git-based workflow and experience shipping through CI/CD
Excellent communication skills, including explaining pipeline issues to non-technical stakeholders
Exceptional analytical and problem-solving skills, including root-cause analysis
Strong knowledge and experience working with customers in a consultative technical environment
Streaming experience with Kafka or Event Hubs
Experience with Delta Lake or Iceberg
Infrastructure as code with Terraform or Bicep and containerization with Docker or Kubernetes
Experience in regulated environments involving HIPAA, SOC 2, PCI, or GDPR
Experience building ML data platforms or supporting feature pipelines
Ability to manage multiple client projects and deliver high-quality results on time
Experience with Azure DevOps or GitHub for source control and pipelines