Senior Equities Data Engineer

Balyasny Asset Management L.P.; Chicago (IL); On-site; USD 130,000 - 190,000; Full time

Job summary

Balyasny Asset Management L.P. is seeking a data engineer to design and scale the Equities Alternative Data platform. You will own ingestion, normalization, and backfills for diverse datasets, building robust ETL/ELT pipelines and embedding quality controls across the data stack.

You will collaborate with portfolio managers, quantitative researchers, and analysts to convert investment use cases into scalable technical solutions, delivering researcher-friendly data access APIs and curated tables.

Qualifications

• 5+ years in a data-intensive engineering role, building and operating data pipelines, platforms, or data products.
• Strong SQL skills with time-series/analytical data stores.
• Proficient in Python and/or Rust/C++, plus data-manipulation libraries (Pandas, Polars, PySpark, Dask).
• Experience designing scalable ETL/ELT pipelines (batch and near-real-time).
• Familiarity with Parquet/Arrow storage formats.
• Good debugging/production problem-solving skills.
• Exposure to distributed data components (Kafka, Redis, orchestration tools).
• Ability to work with tech and non-tech stakeholders on trade-offs.
• Ownership mindset with focus on data quality.

Responsibilities

• Design, build, and scale the data platform and pipelines for alternate-data research and production use cases.
• Own dataset onboarding, including ingestion, schema design, normalization, and backfills.
• Develop scalable ETL/ELT pipelines for diverse datasets (structured, semi-structured, unstructured).
• Leverage in-house data-quality framework to monitor timeliness, completeness, accuracy, continuity.
• Collaborate with portfolio managers, analysts, and researchers to translate use cases into technical solutions.
• Create researcher-friendly data access patterns: APIs, curated tables, data extracts, integrations with research tools.
• Collaborate with external data vendors and internal partners to improve reliability.
• Contribute to dataset documentation, governance, entitlement controls, and auditability.
• Balance feasibility, stability, scalability, data quality, and time-to-market in a fast-paced environment.
• Participate in a global engineering team, including code reviews and platform improvements.

Skills

SQL + time-series DBs

Python, Rust or C++

Pandas/Polars/PySpark

ETL/ELT pipelines

Parquet/Arrow

Debugging in prod

Kafka/Redis

Stakeholder comms

Data quality ownership

Tools

Kafka

Redis

Airflow

Dagster

dbt

Prometheus/Grafana

Job description

The Equities Alternative Data team is responsible for sourcing, integrating, curating, and delivering differentiated alternative datasets that support BAM’s Equities Long/Short investment teams. We build and operate the data products, platforms, and workflows that enable portfolio managers, analysts, and quantitative researchers to efficiently discover, evaluate, back-test, and consume high-quality alternative data signals.

We are looking for an experienced, pragmatic, and eager data engineer to join the team. This individual will help design and scale the data infrastructure underpinning our alternative-data offering—from vendor onboarding and ingestion through normalization, entity mapping, quality controls, historical backfills, and researcher-facing delivery.

Key responsibilities:

• Design, build, and grow the data platform and pipelines that support alternative-data research and production use cases for Equities L/S teams
• Own end-to-end dataset onboarding, including ingestion, schema design, normalization, historical backfills, metadata, lineage, and access patterns
• Build scalable, reliable ETL/ELT workflows for large, diverse, and frequently changing datasets, including structured, semi-structured, and unstructured sources
• Leverage and extend BAM’s in-house data-quality framework to monitor, reconcile, and alert on the timeliness, completeness, accuracy, and continuity of critical alternative datasets
• Partner closely with portfolio managers, fundamental analysts, quantitative researchers, and data-product stakeholders to translate investment use cases into robust technical solutions
• Design researcher-friendly data access and consumption patterns, including APIs, curated tables, data extracts, and integrations with research and back-testing environments
• Collaborate with external data vendors and internal technology partners to evaluate feeds, resolve data issues, and improve operational reliability
• Contribute to standards for dataset documentation, discoverability, governance, entitlement controls, and auditability
• Balance feasibility, stability, scalability, data quality, and time-to-market when delivering solutions in a fast-paced investment environment
• Participate in a global engineering team, contributing to code reviews, architecture discussions, operational support, and shared platform improvements

QUALIFICATIONS & REQUIREMENTS:

• 5+ years of experience in a data-intensive engineering role, building and operating data pipelines, platforms, or data products
• Demonstrable expertise in SQL and databases, particularly time-series, analytical, and large-scale data stores
• Strong programming skills in Python and/or Rust/C++, plus experience with data-manipulation libraries or frameworks such as Pandas, Polars, PySpark, Dask, or similar
• Experience architecting and building scalable ETL/ELT pipelines, including batch processing and, ideally, streaming or near-real-time workflows
• Experience with column-oriented storage and serialization formats such as Parquet and Arrow
• Strong debugging skills across the application and data stack, with demonstrated problem-solving ability in production environments
• Experience with code optimization and performance tuning for large-scale data processing workloads
• Familiarity with distributed-system data components, such as Kafka, Redis, workflow orchestration tools, messaging systems, or caching layers
• Proven ability to work directly with technical and non-technical stakeholders and communicate trade-offs clearly
• Strong ownership mindset, attention to data quality, and ability to deliver in an iterative, business-focused environment

Additional experience in the following areas is a plus:

• Experience working with alternative data, including consumer transactions, web data, geolocation, app data, job postings, supply-chain data, sentiment, or other nontraditional datasets
• Familiarity with financial instruments, public-equity research workflows, equity factor research, portfolio management, risk management, or back-testing environments
• Experience with security master, entity resolution, issuer/ticker mapping, point-in-time datasets, corporate actions, or historical identifier management
• Prior experience with ClickHouse, Snowflake, Databricks, Trino, DuckDB, or similar analytical data platforms
• Experience building APIs or application-level services to expose data products and business logic
• Experience with orchestration and transformation tooling such as Airflow, Dagster, dbt, or similar
• Familiarity with observability tooling such as Prometheus, Grafana, Sentry, OpenTelemetry, or distributed tracing and monitoring frameworks
• Experience with distributed stateful stream processing, such as Kafka Streams, Flink, or Arroyo
• Experience with cloud object storage and compute environments, including data access controls, cost management, and production operational practices