Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).
Job Title: Data Engineer (PySpark / Scala / Python)
Location: Remote
Job Description:We are hiring a Data Engineer with strong hands-on experience in PySpark, Scala, and Python. You must have solid expertise in Apache Spark, as it will be the core technology used for building and managing large-scale data processing pipelines.
Experience with cloud platforms like Google Cloud Platform (GCP), Microsoft Azure, or AWS is a plus.
Required Skills:Strong hands-on experience with Apache Spark
Proficient in PySpark
Experience in Scala and Python
Knowledge of ETL processes and data pipeline design
Understanding of distributed data processing
Familiarity with version control tools like Git
Basic knowledge of cloud platforms (GCP, AWS, or Azure)
Experience with cloud-native data tools (e.g., Dataproc, Glue, EMR, BigQuery)
Familiarity with workflow/orchestration tools like Airflow or Cloud Composer
Experience with CI/CD for data engineering
Exposure to both structured and unstructured data
Similar Jobs
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Designs and leads scalable Azure data platforms, ETL/ELT pipelines, Spark workflows, AI integrations, and cloud applications. Responsibilities include data modeling, advanced SQL optimization, reliability improvements, API integration, AI-powered automation, architectural standards, stakeholder analysis, mentoring, and evaluating emerging technologies. The role uses Azure Databricks, Snowflake, Spark, Python, and modern software engineering practices to support analytics, machine learning, business intelligence, and healthcare data solutions.
Top Skills:
Ai AgentsSparkAutomated TestingAzureAzure DatabricksAzure FunctionsAzure Service BusCi/CdGenerative AiGitGithub ActionsGraphQLInfrastructure As CodeLakebaseLlmsMicroservicesPysparkPythonRestSnowflakeSQLTerraformUnity Catalog
Artificial Intelligence • Cloud • Payments • Software • Business Intelligence • Generative AI • Automation
Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.
Top Skills:
DbtGcp Bigquery
Financial Services
Develops secure, scalable data pipelines and analytics solutions using AWS, PySpark, Python, ETL tools, and modern data lakes. Responsibilities include data extraction and transformation, quality monitoring, automation, troubleshooting, system design, code review, operational stability, and collaboration with technical and business stakeholders. The role also supports continuous delivery, application resiliency, security, and data availability across financial services initiatives.
Top Skills:
Ab InitioAirflowAWSCi/CdData LakesDatabricksETLInformaticaJavaPysparkPythonSnowflakeSQL
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
