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Data Engineer at Solvative

Roles & Responsibilities

  • Strong SQL proficiency
  • Experience in Python or Scala/Java
  • Familiarity with ETL/ELT tools
  • Knowledge of cloud platforms (AWS, GCP, Azure)

Requirements:

  • Design, build, and optimize scalable data pipelines
  • Collaborate with cross-functional teams for data solutions
  • Ensure performance and operational excellence
  • Maintain data quality and governance

Job description

Job Title: Data Engineer

Location: Remote

Headquarters: Dallas, USA

Development Center: Ahmedabad

Job Summary:We are seeking a Data Engineer with strong SQL and programming skills (Python/Scala/Java) to design, build, and optimize scalable data pipelines and data platforms. The ideal candidate has experience with ETL/ELT frameworks, distributed processing technologies such as Spark, and cloud-based data services. You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while ensuring performance, governance, and operational excellence.

Experience:
2 years

Core technical skills

  • SQL proficiency (non-negotiable; look for window functions, query optimization, not just basic SELECTs)
  • At least one strong programming language (Python or Scala/Java)
  • Data pipeline/ETL-ELT tools (Airflow, dbt, Dagster, Luigi)
  • Distributed processing frameworks (Spark, Flink, sometimes Hadoop for legacy)
  • Cloud platforms (AWS, GCP, or Azure) and their data services (Redshift, BigQuery, Snowflake, Databricks)

Data modeling & architecture

  • Understanding of dimensional modeling, normalization, star/snowflake schemas
  • Experience with both batch and streaming architectures
  • Knowledge of data warehousing vs. data lake vs. lakehouse concepts
  • Familiarity with file formats (Parquet, Avro, ORC) and why they matter

Engineering fundamentals (separates good from average)

  • Version control (Git), CI/CD practices
  • Code quality: testing, modularity, documentation
  • Infrastructure-as-code (Terraform) and containerization (Docker, Kubernetes)
  • Understanding of data quality, observability, and monitoring

Signals of depth vs. surface knowledge

  • Can they explain why they chose a tool, not just that they used it?
  • Evidence of handling scale (data volumes, latency requirements, cost optimization)
  • Ownership of end-to-end pipelines vs. only writing isolated scripts
  • Awareness of data governance, lineage, and privacy/compliance

Soft signals

  • Collaboration with analysts, data scientists, and business stakeholders
  • Ability to translate ambiguous requirements into reliable pipelines
  • Communication clarity in how they describe past work

Watch-outs

  • Buzzword stuffing without depth (lists every tool but can’t go deep on any)
  • Only coursework/certifications with no applied projects
  • Confusing data engineering with data analysis or pure ML work

Why Solvative?

  • Top-of-the-line Apple laptops for increased mobility and better productivity.
  • Medical insurance for all permanent employees.
  • The opportunity of working with an organization that believes in investing in employees’ growth.
  • An informal work environment that enables you to have fun while being productive.
  • Lots and lots of fun activities: we take over one of the nearby restaurants every last Friday of the month, tickets to all Marvel movies for the entire team, company picnics, and more!

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